DESC:Field of the Invention
The present invention relates to photovoltaic solar plant monitoring and protection systems, and more particularly to a system and method for AI-based fault panel identification, predictive hotspot detection, collaborative fault validation, autonomous protection control, and lifecycle management in photovoltaic solar plants.

Background of the Invention
Photovoltaic solar plants are increasingly utilized for large-scale and distributed power generation owing to growing global demand for renewable energy infrastructure and sustainable energy systems. Modern photovoltaic installations commonly include large numbers of interconnected photovoltaic panels arranged in string-based architectures and operating under continuously varying environmental and electrical conditions. Reliable operation of such photovoltaic systems requires accurate panel-level monitoring, fault identification, predictive protection, and lifecycle assessment to ensure uninterrupted power generation and operational stability.
Conventional monitoring approaches generally perform supervision at inverter level or string level, thereby limiting accurate localization and identification of abnormalities associated with individual photovoltaic panels. Consequently, localized operating irregularities may remain undetected for extended durations, resulting in gradual efficiency degradation, reduction in energy generation capability, irreversible panel deterioration, and potential large-scale operational disruption.
Further, photovoltaic panels arranged in interconnected string configurations are highly susceptible to propagation of thermal and electrical abnormalities originating from a single affected photovoltaic panel. Such abnormal operating behavior may progressively influence neighboring photovoltaic panels and associated electrical infrastructure, thereby increasing risks of operational instability, cascading failure conditions, equipment damage, and safety hazards within the photovoltaic solar plant. Known fault detection approaches predominantly rely on static threshold-based monitoring and centralized supervisory architectures, which are generally inadequate for identifying dynamically evolving operating conditions under continuously changing environmental and operational scenarios. Such approaches are typically reactive in nature and lack predictive analytical capability for identifying progressive abnormalities, hidden fault conditions, degradation progression, or future operational risks associated with photovoltaic panels.
Another technical challenge arises from dependence on localized monitoring hardware associated with corresponding photovoltaic panels. Failure of a monitoring unit, communication interruption, sensing inaccuracy, or processing malfunction may prevent accurate detection of abnormal operating conditions associated with the photovoltaic panel. As a result, hidden thermal or electrical abnormalities may continue to develop without detection, thereby increasing risks of severe panel degradation, propagation of failures across adjacent photovoltaic panels, reduction in operational reliability, and interruption of photovoltaic string performance.
Additionally, conventional protection mechanisms generally isolate affected photovoltaic panels only after occurrence of severe operating conditions and often fail to preserve electrical continuity across remaining photovoltaic panels associated with the photovoltaic string. Such interruption of current flow may adversely impact operation of healthy photovoltaic panels and reduce overall energy generation efficiency of the photovoltaic solar plant.
Moreover, conventional photovoltaic management frameworks generally lack integrated predictive lifecycle assessment mechanisms capable of estimating future operational condition and remaining useful life associated with photovoltaic panels based on long-term operational behavior and degradation characteristics. Absence of such predictive intelligence limits preventive maintenance capability, operational optimization, and long-term photovoltaic asset reliability.
Accordingly, there exists a need for an advanced photovoltaic monitoring and protection framework capable of intelligent fault prediction, collaborative fault validation, autonomous protection control, and predictive lifecycle management for improving reliability, safety, and operational efficiency of photovoltaic solar plants.

Objective of the Invention
The principal objective of the present invention is to provide a method and system for fault panel identification and lifecycle management in photovoltaic solar plants through intelligent panel-level monitoring and predictive fault analysis.
Another objective of the present invention is to provide collaborative neighboring panel-assisted fault validation for improving detection reliability of hidden abnormalities, hotspot conditions, and monitoring unit failures associated with photovoltaic panels.
Another objective of the present invention is to provide autonomous protection control configured to selectively isolate or bypass affected photovoltaic panels while maintaining photovoltaic string continuity and operational stability of the photovoltaic solar plant.
Another objective of the present invention is to provide predictive lifecycle management and remaining useful life estimation for improving preventive maintenance capability, operational reliability, and energy generation efficiency associated with photovoltaic solar plants.
Another objective of the present invention is to provide intelligent prediction of hotspot escalation and cascading failure propagation associated with interconnected photovoltaic panels operating within a photovoltaic string architecture.
A further objective of the present invention is to provide a distributed monitoring framework capable of identifying abnormal operating conditions based on neighboring panel behavior, communication activity, and operational correlation analysis.

Summary of the Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
The present invention relates to a system and method for AI-based fault panel identification and lifecycle management in photovoltaic solar plants. More particularly, the present invention relates to intelligent panel-level monitoring, predictive fault analysis, collaborative fault validation, autonomous protection control, and predictive lifecycle management associated with interconnected photovoltaic panels operating within photovoltaic string architectures.
In one aspect, the disclosed framework comprises a plurality of solar panel monitoring units associated with corresponding photovoltaic panels and configured to continuously monitor electrical, thermal, operational, and performance-related information associated with the photovoltaic panels. Expected operating behavior associated with a photovoltaic panel is determined and compared with actual operating conditions to identify abnormal operating behavior associated with the photovoltaic panel. AI-based predictive analysis is performed using monitored operating parameters, historical operating behavior, neighboring panel behavior, communication activity, heartbeat information, environmental conditions, and operational variation trends to predict hotspot escalation, cascading failure propagation, degradation progression, hidden fault conditions, or monitoring unit failures associated with photovoltaic panels.
Further, collaborative fault validation is performed by correlating neighboring photovoltaic panel behavior, communication status information, thermal propagation trends, electrical operating conditions, and heartbeat activity associated with adjacent photovoltaic panels. Such collaborative fault validation enables identification of hidden abnormalities and improves reliability of abnormality detection during monitoring unit malfunction, communication interruption, or progressive operational degradation associated with photovoltaic panels.
In another aspect, responsive protection actions are autonomously initiated to selectively isolate or bypass affected photovoltaic panels while maintaining electrical continuity across photovoltaic strings. The disclosed framework thereby minimizes propagation of thermal and electrical abnormalities across interconnected photovoltaic panels, reduces interruption of current flow, and improves operational stability and fault tolerance associated with photovoltaic solar plants. Monitoring activity associated with neighboring photovoltaic panels may be dynamically increased responsive to predicted abnormal operating conditions, and isolated photovoltaic panels may be selectively reconnected upon satisfaction of predefined operating criteria.
Further, predictive lifecycle management and remaining useful life estimation associated with photovoltaic panels are performed based on degradation behavior, thermal stress history, environmental exposure, operational trends, and fault occurrence history associated with the photovoltaic panels.
Accordingly, the disclosed system and method provide an intelligent and distributed photovoltaic monitoring and protection architecture capable of predictive fault analysis, collaborative neighboring panel-assisted fault validation, hidden abnormality detection, autonomous protection control, cascading failure prevention, photovoltaic string continuity preservation, and predictive lifecycle management associated with photovoltaic solar plants.

Brief description of the drawings
The figures described below depict various aspects of the system and methods disclosed herein. It should be understood that each figure depicts an embodiment of a particular aspect of the disclosed system and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals.
FIG. 1 illustrates a block diagram of an AI-based photovoltaic monitoring and protection system architecture (100) for panel-level monitoring and distributed photovoltaic protection, according to one embodiment of the present invention.
FIG. 2 depicts a block diagram of a predictive analytics platform (200) configured for AI-based fault prediction and protection control, according to one embodiment of the present invention.
FIG. 3 illustrates a flow diagram (300) of an AI-based hotspot identification and photovoltaic protection process for predictive fault detection and intelligent panel protection, according to one embodiment of the present invention.
FIG. 4 presents a flow diagram of a method (400) for AI-based fault panel identification, efficiency monitoring, predictive fault analysis, and lifecycle management in photovoltaic solar plants, according to one embodiment of the present invention.
FIG. 5 is a diagrammatic representation (500) of a machine in the example form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed.
Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may have not been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure.
Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.

Detailed Description of the Invention
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary.
Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used by the inventor to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.
It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces.
By the term “substantially” it is meant that the recited characteristic, parameter, or value need not be achieved exactly, but that deviations or variations, including for example, tolerances, measurement error, measurement accuracy limitations and other factors known to those of skill in the art, may occur in amounts that do not preclude the effect the characteristic is intended to provide.
Figures discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way that would limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system. The terms used to describe various embodiments are exemplary. It should be understood that these are provided to merely aid the understanding of the description, and that their use and definitions, in no way limit the scope of the invention. Terms first, second, and the like are used to differentiate between objects having the same terminology and are in no way intended to represent a chronological order, unless where explicitly stated otherwise. A set is defined as a non-empty set including at least one element.
The present invention provides an AI-based photovoltaic monitoring and protection framework configured for intelligent fault panel identification, predictive fault analysis, collaborative fault validation, autonomous protection control, and predictive lifecycle management associated with photovoltaic solar plants.
The disclosed framework utilizes distributed solar panel monitoring units to continuously monitor operating parameters associated with photovoltaic panels and perform AI-based predictive analysis for identifying abnormal operating conditions, hotspot escalation, cascading failure propagation, hidden fault conditions, or monitoring unit failures. Responsive protection actions are autonomously initiated to selectively isolate or bypass affected photovoltaic panels while maintaining photovoltaic string continuity and improving operational reliability associated with photovoltaic solar plants.
FIG. 1 illustrates a block diagram of an AI-based photovoltaic monitoring and protection system architecture (100) for panel-level monitoring and distributed photovoltaic protection. The system architecture (100) comprises a plurality of solar photovoltaic panels including a first to Nth solar photovoltaic panel (101, 102, 103), each solar photovoltaic panel is individually associated with a corresponding Solar Panel Monitoring Unit (SPMU) (104, 105, 106), thereby forming a distributed panel-level monitoring architecture. Such arrangement enables each photovoltaic panel to operate as an independently monitored intelligent node within the photovoltaic monitoring and protection framework, thereby enabling localized monitoring, intelligent fault analysis, distributed protection operation, exact faulty panel identification, and scalable deployment across photovoltaic solar plants.
The first SPMU (104) comprises a sensor module (107a), a processing module (108a), a communication module (109a), and a protective module (110a). Similarly, the second SPMU (105) comprises a sensor module (107b), a processing module (108b), a communication module (109b), and a protective module (110b), while the Nth SPMU (106) comprises a sensor module (107c), a processing module (108c), a communication module (109c), and a protective module (110c).
The sensor modules (107a, 107b, 107c) may comprise a voltage sensor, a current sensor, a temperature sensor, a power monitoring sensor, an efficiency monitoring sensor, or combinations thereof configured to continuously acquire operating parameters associated with corresponding photovoltaic panels (101, 102, 103). The operating parameters may comprise voltage information, current information, temperature information, power output information, efficiency information, operational status information, or combinations thereof associated with the photovoltaic panels. The sensor modules (107a, 107b, 107c) may further perform periodic monitoring, event-triggered monitoring, abnormality-triggered monitoring, and real-time operating condition acquisition associated with the photovoltaic panels.
The monitored operating parameters are transmitted to corresponding processing modules (108a, 108b, 108c). The processing modules (108a, 108b, 108c) are configured to perform local preprocessing, anomaly detection, power computation, thermal trend analysis, localized operating behavior analysis, edge-level intelligent processing, and local decision support generation associated with corresponding photovoltaic panels.
Further, the communication modules (109a, 109b, 109c) may comprise wireless transceivers configured for LoRa communication, NB-IoT communication, wireless telemetry transmission, neighboring panel communication, heartbeat exchange, communication status verification, and distributed operating information exchange between adjacent SPMUs (104, 105, 106). The neighboring verification links established between adjacent SPMUs enable collaborative monitoring, distributed fault validation, and cooperative intelligence between adjacent photovoltaic panels.
According to another implementation, neighboring Solar Panel Monitoring Units (104, 105, 106) collaboratively validate abnormal operating conditions associated with adjacent photovoltaic panels when a corresponding SPMU becomes partially or fully unresponsive. Such collaborative validation may comprise neighboring thermal comparison, heartbeat verification, communication status verification, current mismatch analysis, hidden hotspot validation, neighboring operating condition correlation, and monitoring unit failure detection, thereby enabling fault-tolerant distributed monitoring and self-verifying protection operation associated with the photovoltaic monitoring framework.
The communication modules (109a, 109b, 109c) further transmit monitoring information and operational data to a gateway/edge node (114). The gateway/edge node (114) is configured to aggregate telemetry information received from the plurality of SPMUs (104, 105, 106) and transmit the aggregated information to a Predictive Analytics Platform (115).
The Predictive Analytics Platform (115) is configured to perform AI-based predictive hotspot analysis, cascading failure prediction, degradation trend analysis, remaining useful life estimation, maintenance recommendation generation, protection status analysis, alert generation, and lifecycle management associated with photovoltaic panels. Detailed AI-based predictive analysis and protection orchestration associated with the Predictive Analytics Platform (115) are further described with respect to FIG. 2.
The protective module (110a) comprises a switch (111a), a MOSFET driver/relay (112a), and a bypass path control module (113a). Similarly, the protective module (110b) comprises a switch (111b), a MOSFET driver/relay (112b), and a bypass path control module (113b), while the protective module (110c) comprises a switch (111c), a MOSFET driver/relay (112c), and a bypass path control module (113c).
The protective modules (110a, 110b, 110c) are configured to autonomously initiate responsive protection actions associated with corresponding photovoltaic panels responsive to predicted abnormal operating conditions. The switches (111a, 111b, 111c), MOSFET drivers/relays (112a, 112b, 112c), and bypass path control modules (113a, 113b, 113c) may perform selective photovoltaic panel isolation, intelligent bypass activation, relay-based switching, MOSFET switching, semiconductor switching, or solid-state protection control associated with photovoltaic panels.
According to another implementation, the bypass path control modules (113a, 113b, 113c) are configured to dynamically redirect photovoltaic string current around a faulty photovoltaic panel while maintaining operational continuity across remaining healthy photovoltaic panels associated with the photovoltaic string. Such arrangement prevents complete string shutdown, minimizes propagation of abnormal operating conditions across interconnected photovoltaic panels, and improves operational stability and fault tolerance associated with the photovoltaic solar plant. Detailed protection operation and autonomous bypass control are further described with respect to FIG. 3.
The photovoltaic panels (101, 102, 103) are electrically connected to an inverter/string unit (117), which is configured to perform photovoltaic string interfacing and electrical power conversion associated with the photovoltaic solar plant. The inverter/string unit (117) further provides AC output (118) for supplying electrical power to user equipment (116) and associated electrical infrastructure.
Accordingly, the AI-based photovoltaic monitoring and protection system architecture (100) provides distributed intelligent monitoring, collaborative fault validation, autonomous protection control, photovoltaic string continuity preservation, predictive lifecycle management, and fault-tolerant operation associated with photovoltaic solar plants.
FIG. 2 depicts a block diagram of a predictive analytics platform (200) configured for AI-based fault prediction and protection control. The Predictive Analytics Platform (115) is configured to receive telemetry information, operating parameters, neighboring panel operating information, communication heartbeat information, environmental condition information, and fault-related information from distributed Solar Panel Monitoring Units through the gateway/edge node (114) described with respect to FIG. 1.
The Predictive Analytics Platform (115) comprises a Data Processing and Ingestion module (201), an AI/ML Analytics Engine (207), a Decision Engine (211), and a Protection and Control module (215). The Predictive Analytics Platform (115) further communicates with user equipment (116) for monitoring visualization, alert reporting, maintenance management, lifecycle analysis, and photovoltaic protection supervision.
The Data Processing and Ingestion module (201) comprises a Data Ingestion module (202), a Stream Processing Engine (203), a Data Validation and Cleaning module (204), a Data Normalization and Enrichment module (205), and a Data Storage module (206).
The Data Ingestion module (202) is configured to continuously receive photovoltaic operating information transmitted from distributed Solar Panel Monitoring Units associated with photovoltaic panels. The received operating information may comprise voltage information, current information, temperature information, power output information, efficiency information, neighboring panel operating information, communication heartbeat information, environmental operating information, thermal trend information, and fault-related telemetry information associated with photovoltaic panels.
The Stream Processing Engine (203) is configured to process continuously received photovoltaic operating information for real-time operating condition monitoring, abnormality identification, thermal variation analysis, and event-driven operating analysis associated with photovoltaic panels.
The Data Validation and Cleaning module (204) is configured to validate received operating information, identify corrupted telemetry information, remove incomplete operating records, verify communication consistency, reduce false alarm generation, and improve integrity of photovoltaic operating information prior to predictive analysis.
The Data Normalization and Enrichment module (205) is configured to normalize photovoltaic operating information using irradiance-adjusted normalization, environmental condition compensation, neighboring panel operating correlation, and temperature-compensated operating analysis associated with photovoltaic panels. The Data Normalization and Enrichment module (205) further enriches operating information using historical operating behavior, thermal trend history, environmental operating conditions, neighboring photovoltaic panel behavior, and lifecycle operating information.
In one embodiment, expected photovoltaic power output associated with a photovoltaic panel may be determined according to:
P_expected=P_rated×G/G_ref ×(1-a(T-T_ref))
where P_expected represents expected photovoltaic power output, P_rated represents rated photovoltaic power output associated with the photovoltaic panel, Grepresents measured irradiance, G_ref represents reference irradiance, T represents measured photovoltaic operating temperature, T_ref represents reference operating temperature, and arepresents a photovoltaic temperature coefficient. Such arrangement enables irradiance-adjusted and temperature-compensated operating normalization for improving photovoltaic fault detection accuracy under dynamically varying environmental operating conditions.
The processed operating information is stored within the Data Storage module (206) for historical degradation learning, lifecycle analytics support, fault trend analysis, event history retention, predictive model training, and long-term photovoltaic operational analysis. Accordingly, photovoltaic operating information received from distributed Solar Panel Monitoring Units is continuously processed, validated, normalized, and enriched prior to AI-based predictive analysis.
The processed operating information is transmitted to the AI/ML Analytics Engine (207). The AI/ML Analytics Engine (207) comprises an AI/ML Model module (208), a Collaborative Fault Validation module (209), and an Analytics Services module (210).
The AI/ML Model module (208) is configured to perform predictive analysis using real-time operating information, historical operating behavior, neighboring panel operating information, environmental operating conditions, communication heartbeat information, and thermal variation trends associated with photovoltaic panels. The AI/ML Model module (208) may perform predictive hotspot escalation analysis, degradation prediction, hidden fault prediction, anomaly detection, cascading failure prediction, contextual operating analysis, and thermal trend learning associated with photovoltaic panels.
In an embodiment, actual photovoltaic power output associated with a photovoltaic panel may be determined according to:
P_actual=V×I
where P_actual represents actual photovoltaic power output, V represents measured photovoltaic voltage, and I represents measured photovoltaic current associated with the photovoltaic panel. The determined actual photovoltaic power output may be utilized for predictive hotspot analysis, degradation trend analysis, neighboring panel operating comparison, and abnormal operating condition identification associated with photovoltaic panels.
The AI/ML Analytics Engine (207) further performs supervised learning, unsupervised learning, adaptive learning model processing, and time-series analysis for identifying abnormal photovoltaic operating behavior prior to catastrophic panel failure. The AI/ML Analytics Engine (207) may further differentiate temporary environmental operating conditions from persistent abnormal operating conditions associated with photovoltaic panels. The AI/ML Analytics Engine (207) may additionally identify gradual degradation conditions including progressive dust accumulation, thermal instability, or abnormal electrical operating behavior using temperature-compensated and context-aware operating analysis.
The Analytics Services module (210) is configured to generate predictive hotspot information, degradation trend information, operational health information, maintenance recommendation information, lifecycle analytics information, remaining useful life estimation information, and photovoltaic fault prediction information associated with photovoltaic panels.
The Collaborative Fault Validation module (209) is configured to perform distributed fault validation using neighboring photovoltaic panel behavior, communication-loss verification, heartbeat monitoring, neighboring abnormality correlation, thermal propagation analysis, electrical operating correlation, hidden hotspot identification, and unresponsive Solar Panel Monitoring Unit analysis associated with adjacent photovoltaic panels.
In one implementation, neighboring photovoltaic panel deviation analysis may be determined according to:
S_i=(P_i-µ_p)/s_p
where S_i represents statistical operating deviation associated with a photovoltaic panel, P_i represents operating performance associated with the photovoltaic panel, µ_p represents mean operating performance associated with neighboring photovoltaic panels, and s_p represents statistical deviation associated with neighboring photovoltaic panel operating behavior. Such arrangement enables collaborative neighboring panel-assisted fault validation, hidden abnormality identification, and distributed photovoltaic fault analysis independent of transient environmental operating variations.
According to another implementation, fault conditions associated with a photovoltaic panel may be collaboratively validated using neighboring photovoltaic panel behavior when the corresponding Solar Panel Monitoring Unit becomes unavailable or unresponsive. Such arrangement enables distributed fault tolerance, self-validating monitoring, cooperative abnormality detection, and thermal propagation intelligence associated with photovoltaic solar plants. Detailed operational sequencing associated with collaborative fault validation, predictive hotspot identification, and autonomous photovoltaic protection control is further described with respect to FIG. 3.
The processed predictive analytics information is transmitted to the Decision Engine (211). The Decision Engine (211) comprises a Risk Assessment module (212), a Rule Engine (213), and a Decision Manager (214).
The Risk Assessment module (212) is configured to perform hotspot severity estimation, cascading failure prediction, photovoltaic operational instability analysis, thermal propagation risk estimation, and fire risk analysis associated with photovoltaic panels.
The Rule Engine (213) is configured to apply adaptive protection thresholds, environmental-condition-aware protection logic, AI-assisted decision policies, fault management rules, operational decision criteria, and contextual fault prioritization associated with photovoltaic solar plants. The Rule Engine (213) may further dynamically adapt protection thresholds responsive to environmental operating conditions, thermal operating variations, neighboring panel behavior, and photovoltaic operating instability.
The abnormal photovoltaic fault conditions may be identified responsive to simultaneous occurrence of electrical operating deviation exceeding a predefined deviation threshold and thermal operating variation exceeding a predefined thermal threshold, thereby reducing false abnormality detection associated with temporary environmental operating variations, irradiance fluctuation conditions, or transient shading conditions.
The Decision Manager (214) is configured to generate protection decisions, monitoring escalation decisions, maintenance decisions, reconnection decisions, and autonomous fault response actions associated with photovoltaic panels. Accordingly, protection decisions are dynamically generated responsive to predicted hotspot escalation probability, cascading failure risk, neighboring thermal propagation behavior, and photovoltaic operational instability.
The generated decision information is transmitted to the Protection and Control module (215). The Protection and Control module (215) comprises a Protection Action Generator (216), a Control Command Manager (217), and a Device Interface Service module (218).
The Protection Action Generator (216) is configured to generate autonomous protection instructions associated with predicted abnormal operating conditions, hotspot escalation, hidden hotspot conditions, cascading failure propagation, or monitoring unit failures associated with photovoltaic panels.
The Control Command Manager (217) is configured to generate and manage autonomous bypass commands, relay activation commands, MOSFET control commands, panel isolation commands, monitoring escalation commands, and photovoltaic protection control instructions associated with distributed Solar Panel Monitoring Units.
The Device Interface Service module (218) is configured to securely transmit generated control commands and protection instructions to corresponding Solar Panel Monitoring Units through the gateway/edge node (114). The Device Interface Service module (218) further enables edge-device coordination, command synchronization, operational feedback monitoring, and protection status verification associated with photovoltaic panels.
Accordingly, intelligent protection instructions are autonomously generated and transmitted to distributed Solar Panel Monitoring Units for preventive photovoltaic protection while maintaining photovoltaic string continuity. Such arrangement enables preventive protection operation, autonomous response generation, distributed protection coordination, and continued photovoltaic string operation during photovoltaic fault conditions.
FIG. 3 illustrates a flow diagram (300) of an AI-based hotspot identification and photovoltaic protection process for predictive fault detection and intelligent panel protection. The process (300) enables continuous panel-level monitoring, AI-based predictive fault analysis, collaborative neighboring panel-assisted fault validation, environmental-condition-aware operating analysis, autonomous protection control, intelligent bypass operation, and predictive lifecycle management associated with photovoltaic solar plants. The process (300) may be implemented using the distributed Solar Panel Monitoring Units and the Predictive Analytics Platform described with respect to FIG. 1 and FIG. 2.
At step (301), operating parameters associated with photovoltaic panels are continuously monitored using corresponding Solar Panel Monitoring Units as described with respect to FIG. 1. The monitored operating parameters may comprise voltage information, current information, temperature information, power output information, heartbeat information, operational status information, and other photovoltaic operating parameters associated with corresponding photovoltaic panels. The monitoring operation may comprise periodic monitoring, event-triggered monitoring, abnormality-triggered monitoring, and real-time telemetry acquisition associated with distributed photovoltaic panels.
At step (302), the monitored operating information is transmitted to the gateway/edge node and the Predictive Analytics Platform described with respect to FIG. 1 and FIG. 2. The transmitted operating information may comprise neighboring panel operating information, communication heartbeat information, environmental operating information, thermal operating information, and operational trend information associated with photovoltaic panels for distributed predictive analysis and fault monitoring.
At step (303), actual photovoltaic operating behavior is compared with expected photovoltaic operating behavior to identify abnormal operating conditions associated with photovoltaic panels. The expected operating behavior may be determined using irradiance-adjusted operating estimation, environmental operating conditions, neighboring panel operating correlation, historical operating behavior, and temporal operating analysis associated with photovoltaic panels.
The normalized photovoltaic performance deviation associated with a photovoltaic panel may be determined according to:
D=(P_expected-P_actual)/P_expected
where D represents normalized photovoltaic performance deviation, P_expected represents expected photovoltaic power output, and P_actual represents measured photovoltaic power output associated with the photovoltaic panel. The normalized photovoltaic performance deviation may be utilized for predictive hotspot identification, degradation trend analysis, neighboring operating correlation analysis, and abnormal photovoltaic operating condition detection.
At decision step (304), determination is performed to identify whether abnormal operating behavior is detected. If abnormal operating behavior is not detected, the process proceeds to step (305), wherein continuous photovoltaic monitoring operation is maintained without protection activation.
If abnormal operating behavior is detected at step (304), the process proceeds to step (306), wherein AI-based fault analysis is performed using the Predictive Analytics Platform described with respect to FIG. 2. The AI-based fault analysis may comprise historical operating trend analysis, thermal operating analysis, degradation trend analysis, neighboring operating correlation analysis, environmental operating correlation analysis, and contextual photovoltaic operating analysis associated with photovoltaic panels.
The AI-based fault analysis may identify abnormal operating scenarios including hotspot formation, thermal degradation, dust accumulation, bypass diode fault conditions, connector fault conditions, communication instability, electrical mismatch conditions, or combinations thereof associated with photovoltaic panels. In an exemplary implementation, gradual power reduction associated with substantially stable thermal operating behavior may indicate progressive dust accumulation, while rapidly increasing temperature associated with power degradation may indicate hotspot escalation associated with a photovoltaic panel.
At decision step (307), determination is performed to identify whether detected operating variation is associated with environmental variation conditions. Such environmental variation conditions may comprise temporary cloud movement, transient shading conditions, solar angle variation, irradiance fluctuation, or temporary environmental operating changes associated with photovoltaic panels.
Temporary operating variation caused by transient environmental conditions may be differentiated from persistent photovoltaic fault conditions using AI-based operating analysis, neighboring panel comparison, irradiance-adjusted operating estimation, temporal operating analysis, and contextual environmental operating correlation. If environmental variation is detected at step (307), the process proceeds to step (308), wherein monitoring parameters may be dynamically adjusted and photovoltaic monitoring operation may continue without autonomous protection activation, thereby reducing false alarm generation and unnecessary photovoltaic protection operations.
If persistent abnormal operating conditions are identified at step (307), the process proceeds to step (309), wherein predictive hotspot escalation analysis and cascading failure risk analysis are performed. At step (309), hotspot escalation probability, degradation progression behavior, neighboring thermal propagation behavior, and photovoltaic operational instability associated with photovoltaic panels may be predictively identified prior to catastrophic photovoltaic panel failure.
At decision step (310), determination is performed to identify whether the corresponding Solar Panel Monitoring Unit remains responsive. If the Solar Panel Monitoring Unit is responsive, the process proceeds to decision step (312).
If the Solar Panel Monitoring Unit becomes partially or completely unresponsive, the process proceeds to step (311), wherein neighboring Solar Panel Monitoring Units collaboratively validate abnormal operating conditions associated with adjacent photovoltaic panels.
At step (311), distributed fault validation may be performed using neighboring thermal behavior, communication heartbeat absence, current mismatch behavior, operating inconsistency analysis, neighboring abnormality correlation, communication-loss detection, hidden hotspot identification, and thermal propagation analysis associated with adjacent photovoltaic panels. Neighboring photovoltaic panels may continue monitoring thermal propagation behavior and electrical operating behavior associated with the unresponsive photovoltaic panel to prevent hidden hotspot escalation and cascading failure propagation, thereby enabling fault-tolerant distributed photovoltaic monitoring and self-validating fault analysis. Adjacent photovoltaic panels operating within a common photovoltaic string and substantially similar environmental conditions may exhibit correlated electrical and thermal operating behavior. Accordingly, deviation of operating behavior associated with a particular photovoltaic panel relative to neighboring photovoltaic panels may indicate hidden abnormal operating conditions, monitoring unit malfunction, or hotspot escalation associated with the photovoltaic panel.
At decision step (312), determination is performed to identify whether critical hotspot escalation risk, cascading failure risk, or critical abnormal operating conditions are detected. If critical abnormal operating conditions are not detected, the process proceeds to step (313), wherein photovoltaic monitoring operation continues.
If critical hotspot escalation risk or cascading failure risk is identified at step (312), the process proceeds to step (314), wherein a protection control signal is generated responsive to the identified abnormal operating condition.
At step (315), intelligent bypass protection operation and autonomous photovoltaic protection operation are initiated. Relay switching, MOSFET switching, semiconductor switching, or solid-state protection control may be activated responsive to the generated protection control signal.
At step (316), affected photovoltaic panels may be selectively isolated or bypassed while maintaining photovoltaic string continuity and continued operation of remaining healthy photovoltaic panels associated with the photovoltaic string. Such arrangement enables autonomous protection activation, intelligent bypass operation, selective fault isolation, prevention of complete photovoltaic string shutdown, and continued photovoltaic power delivery during photovoltaic fault conditions.
At step (317), hotspot alerts, protection status information, maintenance recommendation information, degradation information, remaining useful life information, and lifecycle management information associated with photovoltaic panels may be generated and transmitted to user equipment as described with respect to FIG. 2 for photovoltaic monitoring, maintenance supervision, and lifecycle management associated with photovoltaic solar plants.
In another implementation, the disclosed framework may identify gradual dust accumulation associated with photovoltaic panels using long-term operating trend analysis, irradiance-adjusted operating estimation, neighboring panel comparison, and contextual operating correlation analysis. Gradual reduction in power generation efficiency associated with a photovoltaic panel, while maintaining substantially stable thermal behavior and normal communication heartbeat activity, may indicate progressive dust accumulation rather than hotspot escalation or electrical fault conditions. Responsive to identification of dust accumulation conditions, maintenance recommendation information and panel cleaning alerts may be generated while maintaining continued photovoltaic operation without unnecessary autonomous isolation or bypass protection activation.
In a further implementation, temporary environmental operating variations including cloud movement, transient shading conditions, solar angle variation, seasonal irradiance fluctuation, or temporary atmospheric changes may be differentiated from persistent photovoltaic fault conditions using neighboring photovoltaic panel behavior, temporal operating analysis, irradiance-adjusted normalization, and environmental operating correlation analysis. Simultaneous temporary reduction in power generation associated with multiple neighboring photovoltaic panels without corresponding abnormal thermal escalation may be identified as an environmental operating variation rather than an actual photovoltaic fault condition. The disclosed framework may dynamically adjust monitoring sensitivity and operating evaluation criteria responsive to identified environmental conditions, thereby reducing false alarm generation and preventing unnecessary protection activation.
In another embodiment, connector fault conditions or electrical mismatch conditions associated with photovoltaic panels may be identified using current mismatch analysis, thermal operating correlation, voltage instability analysis, neighboring operating comparison, and progressive electrical variation monitoring. Localized thermal escalation combined with unstable electrical operating behavior may indicate connector degradation, loose electrical interconnection, or photovoltaic mismatch conditions. The disclosed framework may generate predictive maintenance recommendations and protection control decisions prior to occurrence of catastrophic electrical failure or cascading thermal propagation.
In another operational scenario, bypass diode fault conditions associated with photovoltaic panels may be identified using thermal propagation analysis, voltage operating analysis, hotspot localization analysis, degradation trend monitoring, and neighboring photovoltaic operating correlation. Abnormal localized heating associated with degraded photovoltaic power generation capability may indicate bypass diode malfunction associated with the photovoltaic panel. The disclosed framework may initiate predictive protection operation and maintenance alert generation responsive to identified bypass diode fault conditions.
According to a further implementation, communication instability or partial monitoring unit malfunction associated with a Solar Panel Monitoring Unit may be identified using heartbeat verification analysis, neighboring communication validation, operating inconsistency analysis, telemetry interruption monitoring, and distributed neighboring photovoltaic panel monitoring. In circumstances where a corresponding Solar Panel Monitoring Unit becomes partially or completely unresponsive, neighboring photovoltaic panels may continue monitoring thermal propagation behavior and electrical operating conditions associated with the unresponsive photovoltaic panel, thereby enabling hidden hotspot identification, communication-loss detection, self-validating fault analysis, and fault-tolerant photovoltaic protection operation.
In another implementation, degradation progression and remaining useful life associated with photovoltaic panels may be estimated using long-term thermal operating trends, environmental exposure information, power degradation behavior, abnormal operating history, lifecycle operating analysis, and predictive degradation learning. Responsive to identified degradation progression, predictive maintenance schedules, lifecycle management information, replacement recommendation information, and photovoltaic health assessment information may be generated for improving long-term operational reliability and energy generation efficiency associated with photovoltaic solar plants.
Accordingly, the process (300) enables AI-based predictive hotspot identification, distributed fault validation, environmental-condition-aware fault differentiation, autonomous photovoltaic protection, intelligent bypass operation, cascading failure prevention, predictive lifecycle management, and fault-tolerant photovoltaic monitoring associated with photovoltaic solar plants.
FIG. 4 presents a flow diagram of a method (400) for AI-based fault panel identification, efficiency monitoring, predictive fault analysis, and lifecycle management in photovoltaic solar plants. The method (400) enables intelligent panel-level monitoring, photovoltaic efficiency monitoring, predictive hotspot identification, collaborative neighboring panel-assisted fault validation, autonomous protection control, and predictive lifecycle management associated with photovoltaic solar plants.
The method (400) may be implemented using distributed Solar Panel Monitoring Units configured for panel-level telemetry acquisition, communication, localized operating analysis, efficiency evaluation, and photovoltaic protection control, together with a predictive analytics platform configured for AI-based predictive fault analysis, risk assessment, efficiency degradation analysis, and autonomous photovoltaic protection orchestration.
At step (405), real-time electrical and thermal parameters associated with a photovoltaic panel are continuously monitored using a Solar Panel Monitoring Unit (SPMU). The monitored operating parameters may comprise voltage information, current information, temperature information, power output information, efficiency information, operational status information, communication heartbeat information, or combinations thereof associated with the photovoltaic panel. The monitoring operation may comprise panel-level monitoring, distributed telemetry acquisition, periodic monitoring, event-triggered monitoring, abnormality-triggered monitoring, and real-time operating analysis associated with photovoltaic panels.
In one embodiment, photovoltaic operating efficiency associated with a photovoltaic panel may be determined according to:
?=P_actual/P_expected ×100
where ?represents photovoltaic operating efficiency, P_actual represents measured photovoltaic power output, and P_expected represents expected photovoltaic power output associated with the photovoltaic panel. The determined photovoltaic operating efficiency may be utilized for degradation progression analysis, lifecycle operating analysis, maintenance recommendation generation, and remaining useful life estimation associated with photovoltaic panels.
At step (410), expected operating behavior associated with the photovoltaic panel is evaluated and compared with actual operating conditions to identify abnormal electrical behavior, thermal operating behavior, degradation progression, or operational instability associated with the photovoltaic panel. The expected operating behavior may be determined using historical operating information, irradiance-adjusted operating estimation, environmental operating conditions, neighboring panel operating correlation, and temporal operating analysis associated with photovoltaic panels.
Transient environmental operating variations may be differentiated from persistent abnormal photovoltaic operating conditions using neighboring photovoltaic panel comparison, contextual environmental operating analysis, irradiance-adjusted operating estimation, and temporal operating correlation associated with photovoltaic panels, thereby reducing false alarm generation and unnecessary photovoltaic protection activation.
At step (415), AI-based predictive analysis is performed using monitored operating parameters, historical operating data, neighboring panel behavior, environmental conditions, communication status information, heartbeat information, and thermal variation trends associated with photovoltaic panels. The predictive analytics platform may perform supervised learning analysis, unsupervised learning analysis, adaptive learning model processing, thermal operating analysis, degradation trend analysis, anomaly detection, neighboring operating correlation analysis, environmental operating correlation analysis, and time-series operating analysis associated with photovoltaic panels.
At step (420), hotspot escalation risk, cascading failure risk, degradation progression, hidden fault conditions, or monitoring unit failure associated with the photovoltaic panel are predictively identified based on the AI-based predictive analysis. The disclosed method enables predictive identification of dangerous photovoltaic operating conditions prior to catastrophic photovoltaic panel failure.
The identified abnormal operating behavior may comprise hotspot formation, thermal degradation, dust accumulation, bypass diode fault conditions, connector fault conditions, communication instability, electrical mismatch conditions, or combinations thereof associated with photovoltaic panels. In one implementation, gradual reduction in photovoltaic power generation associated with substantially stable thermal operating behavior may indicate progressive dust accumulation, while rapidly increasing thermal behavior combined with power degradation may indicate hotspot escalation associated with a photovoltaic panel.
At step (425), collaborative fault validation is performed by correlating neighboring panel behavior, communication activity, heartbeat information, thermal propagation trends, and electrical operating conditions associated with adjacent photovoltaic panels. The collaborative fault validation may further comprise neighboring heartbeat verification, communication-loss detection, hidden hotspot identification, neighboring operating inconsistency analysis, and distributed thermal propagation monitoring associated with photovoltaic panels.
In circumstances where a corresponding Solar Panel Monitoring Unit becomes partially or completely unresponsive, neighboring photovoltaic panels may continue monitoring thermal propagation behavior and electrical operating conditions associated with the unresponsive photovoltaic panel to prevent hidden hotspot escalation and cascading failure propagation. Such arrangement enables distributed fault tolerance, self-validating fault analysis, cooperative photovoltaic monitoring, and neighboring abnormality correlation associated with photovoltaic solar plants.
At step (430), a protection control signal is generated responsive to predicted hotspot escalation, cascading failure propagation, hidden hotspot conditions, or monitoring unit failure associated with the photovoltaic panel. The generated protection control signal may comprise autonomous bypass activation commands, relay switching commands, MOSFET switching commands, panel isolation commands, monitoring escalation commands, or combinations thereof associated with photovoltaic protection operation.
At step (435), responsive protection actions are initiated to autonomously isolate or bypass affected photovoltaic panels while maintaining photovoltaic string continuity. Switches, MOSFET drivers, relay circuitry, bypass circuitry, semiconductor switching circuitry, solid-state protection circuitry, or combinations thereof may be activated responsive to the generated protection control signal. The neighboring panel monitoring frequency may further be dynamically increased responsive to predicted abnormal operating conditions for preventive photovoltaic protection and cascading failure prevention.
The disclosed protection operation enables intelligent bypass operation, selective photovoltaic fault isolation, prevention of complete photovoltaic string shutdown, continued operation of healthy photovoltaic panels, autonomous protection operation, preventive protection control, reduction of cascading failure propagation, and uninterrupted photovoltaic power delivery during photovoltaic fault conditions.
At step (440), remaining useful life information, hotspot risk information, panel health information, degradation information, maintenance recommendation information, protection status information, and lifecycle management information associated with the photovoltaic panel are generated and transmitted to a gateway node, predictive analytics platform, operator dashboard, or user equipment for predictive maintenance planning, photovoltaic health monitoring, maintenance supervision, dashboard alert generation, operator notification, lifecycle management, and operational analysis associated with photovoltaic solar plants.
Long-term photovoltaic operating behavior may be continuously analyzed using thermal stress history, efficiency degradation history, operational reliability analysis, degradation progression analysis, and lifecycle operating analysis to generate predictive maintenance schedules, replacement recommendation information, and lifecycle management information associated with photovoltaic panels, thereby improving long-term photovoltaic plant reliability and operational efficiency.
Accordingly, the method (400) enables AI-based predictive fault analysis, hotspot escalation prediction, distributed collaborative fault validation, autonomous photovoltaic protection, intelligent bypass operation, cascading failure prevention, predictive lifecycle management, predictive maintenance planning, and fault-tolerant photovoltaic monitoring associated with photovoltaic solar plants.
In a further implementation, communication heartbeat timeout conditions associated with a Solar Panel Monitoring Unit may be identified when heartbeat information is not received within a predefined monitoring interval. Responsive to identification of the communication heartbeat timeout condition, neighboring Solar Panel Monitoring Units may autonomously initiate collaborative monitoring, distributed fault validation, and abnormality verification associated with the corresponding photovoltaic panel.
Monitoring frequency associated with photovoltaic panels may be dynamically increased responsive to predicted hotspot escalation risk, abnormal thermal propagation behavior, communication instability, degradation progression, or cascading failure probability in order to improve predictive fault detection accuracy and real-time photovoltaic protection responsiveness.
In another implementation, localized protection decisions may be autonomously generated at a Solar Panel Monitoring Unit using locally evaluated operating conditions, thermal operating behavior, and electrical operating analysis when communication with a predictive analytics platform becomes unavailable or unstable, thereby enabling continued fault-tolerant photovoltaic protection operation during communication interruption conditions.
According to a further implementation, reconnection eligibility associated with an isolated photovoltaic panel may be determined using stabilized thermal behavior, normalized electrical operating conditions, neighboring photovoltaic panel verification, communication heartbeat recovery, and absence of abnormal operating trends prior to restoration of photovoltaic panel operation.
The disclosed framework may further prevent thermal propagation across neighboring photovoltaic panels by autonomously isolating or bypassing affected photovoltaic panels prior to escalation of localized thermal abnormalities, thereby reducing cascading failure propagation and improving photovoltaic plant operational safety.
In another implementation, predictive operating analysis and protection orchestration may be performed using a hybrid edge-cloud architecture in which localized operating analysis and preliminary abnormality detection are performed at distributed Solar Panel Monitoring Units, while computationally intensive predictive analytics, lifecycle analysis, and long-term degradation learning are performed using the Predictive Analytics Platform.
The AI-based predictive models may further be continuously updated using newly acquired operating information, historical fault information, maintenance history information, environmental operating behavior, degradation progression information, and photovoltaic lifecycle operating data associated with photovoltaic panels to improve predictive fault detection accuracy and adaptive operating intelligence over time.
According to another implementation, protection prioritization associated with photovoltaic panels may be dynamically determined based on hotspot severity, thermal propagation probability, cascading failure risk, photovoltaic string criticality, degradation progression behavior, and operational instability associated with photovoltaic panels, thereby enabling intelligent and context-aware photovoltaic protection orchestration.
FIG. 5 is a block diagram of a machine in the example form of a computer system (500) within which instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may comprise a personal computer (PC), tablet device, edge computing device, gateway device, server system, cloud computing device, mobile device, embedded controller, or any machine capable of executing instructions that specify actions to be taken by the machine. The term “machine” shall also be taken to include any collection of machines that individually or jointly execute instructions to perform any one or more of the methodologies discussed herein.
The computer system (500) includes a processor (502), a main memory (504), and a static memory (506), which communicate with each other via a bus (508). The processor (502) may comprise a central processing unit (CPU), graphics processing unit (GPU), microcontroller, edge processor, AI accelerator, or combinations thereof. The processor (502) may execute AI-based photovoltaic fault prediction, hotspot escalation analysis, neighboring fault validation, autonomous protection control, and lifecycle management instructions associated with the photovoltaic monitoring system described herein.
The computer system (500) may further include a user interface (510), an input device (512), a user interface device (514), a drive unit (516), a signal generation device (518), and a network interface device (520). The user interface (510) may display hotspot alerts, panel health information, maintenance recommendations, remaining useful life estimations, protection status information, or predictive analytics associated with photovoltaic panels. The input device (512) and user interface device (514) may facilitate operator interaction, configuration control, maintenance operation, and dashboard access associated with the photovoltaic monitoring system.
The drive unit (516) includes a computer readable medium (522) on which is stored one or more sets of instructions and data structures (524) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions and data structures (524) may also reside, completely or at least partially, within the main memory (504) and/or within the processor (502) during execution thereof by the computer system (500). The main memory (504), static memory (506), and processor (502) may also constitute machine-readable media.
The term “computer readable medium” may include a single medium or multiple media that store instructions for execution by the machine. The term shall accordingly include, but not be limited to, solid-state memories, optical media, magnetic media, flash memory, semiconductor memory devices, removable storage devices, cloud storage resources, or distributed storage systems.
The network interface device (520) may facilitate communication with a communication network (526). The communication network (526) may include a local area network (LAN), wide area network (WAN), Internet, wireless communication network, cloud communication infrastructure, mobile communication network, Wi-Fi network, or combinations thereof. The network interface device (520) may facilitate communication between Solar Panel Monitoring Units (SPMUs), Gateway/Edge Nodes, Predictive Analytics Platforms, User Equipment, cloud servers, and distributed photovoltaic monitoring devices associated with the present invention.
The instructions and data structures (524) may further be transmitted or received over the communication network (526) using any one or more transfer protocols. The instructions may embody AI-based monitoring, predictive hotspot detection, photovoltaic fault analysis, autonomous protection control, neighboring panel validation, cascading failure prediction, and lifecycle management methodologies associated with the present invention.
The disclosed invention provides technical advancements by enabling AI-based predictive hotspot identification, collaborative neighboring panel-assisted fault validation, autonomous photovoltaic protection control, and intelligent bypass operation associated with photovoltaic solar plants. The disclosed framework enables predictive identification of abnormal operating conditions prior to catastrophic photovoltaic panel failure while maintaining photovoltaic string continuity, improving fault tolerance, operational reliability, preventive maintenance capability, safety, and energy generation efficiency.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. ,CLAIMS:We Claim:
1. A method for AI-based fault panel identification, efficiency monitoring and lifecycle management in a photovoltaic solar plant, the method comprising:
continuously monitoring using a solar panel monitoring unit associated with a photovoltaic panel, one or more operating parameters comprising voltage, current, temperature, power output, efficiency information, or combinations thereof;
determining an expected operating performance of the photovoltaic panel and comparing the expected operating performance with actual operating performance to identify an abnormal operating condition associated with the photovoltaic panel;
performing AI-based analysis based on monitored operating parameters, historical operating data, neighboring panel behavior, communication status information, and heartbeat status information associated with one or more neighboring photovoltaic panels to predict hotspot escalation, cascading failure propagation, degradation progression, or monitoring unit failure associated with the photovoltaic panel;
performing collaborative fault validation by correlating thermal behavior, electrical behavior, neighboring panel operating behavior, communication status information, and heartbeat status information associated with the photovoltaic panel and the one or more neighboring photovoltaic panels to identify hidden hotspot conditions associated with the photovoltaic panel; and
predicting a remaining useful life (RUL) of the photovoltaic panel based on degradation behavior, thermal stress history, environmental exposure, and fault occurrence history associated with the photovoltaic panel.

2. The method as claimed in claim 1, further comprising generating a protection control signal, by a predictive analytics platform or a solar panel monitoring unit, responsive to the predicted hotspot escalation, cascading failure propagation, hidden hotspot condition, or monitoring unit failure associated with the photovoltaic panel, and autonomously isolating or bypassing the photovoltaic panel while maintaining electrical continuity across a photovoltaic string.

3. The method as claimed in claim 2, wherein autonomously isolating or bypassing the photovoltaic panel comprises activating a switch, a MOSFET driver/relay, a bypass path control module, or combinations thereof to selectively bypass the photovoltaic panel while maintaining current continuity across remaining photovoltaic panels associated with the photovoltaic string.

4. The method as claimed in claim 1, further comprising comparing the expected operating parameters of the photovoltaic panel with operating parameters, thermal behavior, and communication status information of one or more neighboring photovoltaic panels operating under similar environmental conditions to identify abnormal panel behavior associated with the photovoltaic panel.

5. The method as claimed in claim 1, wherein the AI-based analysis is performed using thermal behavior, degradation history, environmental conditions, operational trends, neighboring panel temperature behavior, mismatch current behavior, temporal temperature variation, power output variation, communication status information, heartbeat status information, or thermal propagation trends associated with the photovoltaic panel and adjacent photovoltaic panels.

6. The method as claimed in claim 1, wherein the expected operating performance of the photovoltaic panel is determined based on irradiance conditions, ambient temperature conditions, panel specification data, weather conditions, and historical operating behavior associated with the photovoltaic panel.

7. The method as claimed in claim 1, wherein the remaining useful life (RUL) is predicted based on efficiency degradation behavior, thermal stress history, environmental exposure, fault occurrence history, or combinations thereof, and further comprising generating a health score, hotspot risk score, degradation score, or combinations thereof associated with the photovoltaic panel.

8. The method as claimed in claim 1, further comprising transmitting monitoring information, hotspot risk information, protection status information, health score information, degradation information, remaining useful life information, communication status information, or heartbeat status information to a gateway/edge node or a predictive analytics platform through a communication module.

9. The method as claimed in claim 1, wherein the abnormal operating condition comprises hotspot formation, hidden hotspot condition, dust accumulation, connector failure, shading condition, bypass diode failure, physical damage, panel degradation, monitoring unit failure condition, or multiple simultaneous fault conditions associated with the photovoltaic panel.

10. A system for AI-based fault panel identification, efficiency monitoring and lifecycle management in a photovoltaic solar plant, the system comprising:
a plurality of photovoltaic panels;
a plurality of solar panel monitoring units (SPMUs), each SPMU associated with a corresponding photovoltaic panel and comprising:
a sensing module configured to monitor operating parameters associated with the corresponding photovoltaic panel;
a processing module configured to determine an expected operating performance of the corresponding photovoltaic panel, identify an abnormal operating condition, and perform AI-based analysis to predict hotspot escalation, cascading failure propagation, degradation progression, or monitoring unit failure associated with the corresponding photovoltaic panel;
a communication module configured to transmit monitoring information and exchange heartbeat status information and neighboring panel health information with one or more neighboring solar panel monitoring units; and
a protective module configured to autonomously isolate or bypass the corresponding photovoltaic panel while maintaining electrical continuity across a photovoltaic string,
a gateway/edge node configured to receive monitoring information from the plurality of solar panel monitoring units; and
a predictive analytics platform comprising a data processing and ingestion module, an AI/ML analytics engine, a collaborative fault validation module, a decision engine, and a protection and control module,
wherein the predictive analytics platform is configured to perform hotspot prediction, cascading failure prediction, hidden hotspot detection, monitoring unit failure detection, collaborative fault validation, remaining useful life (RUL) prediction, and lifecycle management associated with the plurality of photovoltaic panels.