Description:FIELD OF DISCLOSURE
[0001] The present invention relates generally to a multi-sensor fusion-based system and method for early fault detection, classification, and predictive maintenance in hybrid renewable energy source (RES) microgrids, more specifically, relates to timeless fault identification by integrating multi-sensor data fusion, advanced artificial intelligence and machine learning techniques, and hybrid edge-cloud processing architectures to minimize downtime and optimize maintenance strategies.
BACKGROUND OF THE DISCLOSURE
[0002] With the increasing global demand for sustainable energy, hybrid renewable energy source (RES) microgrids that integrate solar, wind, and storage systems have become critical in ensuring energy reliability and sustainability. However, the systems are inherently complex and highly sensitive to environmental fluctuations, load variations, and progressive component degradation. As a result, undetected faults can lead to reduced efficiency, power losses, and unplanned outages.
[0003] Conventional fault detection methods typically rely on fixed thresholds, single-sensor monitoring, or manually supervised approaches. Such methods are often unable to capture emerging, intermittent, or complex fault patterns in dynamic microgrid environments. This limitation results in delayed fault identification, higher false alarm rates, and missed early warning signals, thereby increasing operational inefficiencies, maintenance costs, and system downtime.
[0004] Additionally, existing monitoring solutions are heavily dependent on cloud-only architectures. While cloud-based analytics enable large-scale data handling, they introduce latency, require continuous connectivity, and create potential security vulnerabilities. Standalone monitoring devices and IoT-based solutions have also been introduced in the field; however, they often lack contextual awareness, adaptability, and the intelligence necessary for predictive analysis across diverse renewable energy subsystems.
[0005] Commercial practices currently focus on either isolated fault detection modules or centralized cloud-monitoring platforms. Although these approaches provide partial insights, they generally lack the ability to integrate heterogeneous data sources, adapt to evolving fault scenarios, and deliver real-time, reliable diagnostic insights at scale. Consequently, unexpected downtimes, reduced lifespan of renewable energy assets, and limited scalability remain significant challenges in hybrid microgrid operation.
[0006] The present disclosure attempts to overcome the limitations of conventional approaches to fault management in hybrid renewable energy microgrids. It enables proactive identification of anomalies at an early stage, which reduces the likelihood of critical system failures and minimizes unplanned downtime. Diagnostic accuracy improves progressively through adaptive learning, allowing the framework to accommodate variations in operating conditions, environmental influences, and natural component degradation without the need for manual recalibration. Predictive analytics support timely forecasting of potential failures, thereby facilitating optimal maintenance scheduling and efficient resource utilization. Faults are automatically classified according to severity, impact on power quality, and implications for operational safety, which allows operators to prioritize responses more effectively. By correlating information across electrical, mechanical, thermal, and environmental domains, the disclosure enhances the ability to trace root causes and supports the development of preventive strategies. A continuously evolving repository of fault signatures further strengthens resilience by recognizing recurring as well as previously unseen patterns, contributing to long-term stability and efficiency.
[0007] Thus, in light of the above-stated discussion, there exists a need for system and method for early fault detection and predictive maintenance in hybrid renewable energy microgrid.
SUMMARY OF THE DISCLOSURE
[0008] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0009] According to illustrative embodiments, the present disclosure focuses on a system and method for early fault detection, classification, and predictive maintenance in hybrid renewable energy source (RES) microgrids which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0010] An objective of the present disclosure is to provide system and method for early fault detection, classification, and predictive maintenance in hybrid renewable energy source (RES) microgrid.
[0011] Another objective of the present disclosure is to provide adaptive machine learning models that continuously update fault detection parameters based on operational data.
[0012] Another objective of the present disclosure is to enable predictive fault analytics, wherein potential system anomalies are detected in advance through time-series forecasting, statistical modeling, and degradation analysis.
[0013] Another objective of the present disclosure is to incorporate fault severity classification and prioritization mechanisms that evaluate detected anomalies based on their potential impact on system safety and operational continuity.
[0014] Another objective of the present disclosure is to achieve cross-domain correlation by analyzing electrical, mechanical, thermal, and environmental parameters in an integrated manner to improve root-cause identification and enhance preventive strategies.
[0015] Another objective of the present disclosure is to establish a dynamic fault signature repository that continuously evolves with operational experience, enabling recognition of novel or complex fault patterns across hybrid renewable energy deployments.
[0016] Another objective of the present disclosure is to ensure seamless sensor integration by supporting modular and standardized communication interfaces, thereby enabling flexible scalability and simplified deployment across diverse microgrid configurations.
[0017] Another objective of the present disclosure is to enhance cyber-resilience through anomaly detection in communication channels and validation of sensor data integrity, thereby safeguarding microgrid operations against malicious cyber-physical threats.
[0018] Another objective of the present disclosure is to ensure bandwidth-efficient communication by implementing edge-based data summarization and compressed transmission formats.
[0019] Yet another objective of the present disclosure is to develop a flexible and modular framework that can be easily integrated into current systems and used with a variety of microgrid configurations.
[0020] Yet another objective of the present invention is to provide a system adaptable for deployment in both new and existing microgrid infrastructures, particularly in remote and resource-constrained regions.
[0021] In light of the above, in one aspect of the present disclosure, a system and method for early fault detection and predictive maintenance in hybrid renewable energy microgridsis disclosed herein. The system comprises a plurality of heterogeneous sensors operatively deployed across renewable energy generation units; module is configured to continuously monitor one or more parameters of the microgrids. The system includesa data acquisition and preprocessing module operatively coupled to the sensor module, the data acquisition and preprocessing module configured to collect sensor data, filter noise, normalize values, and handle missing or incomplete data. The system also includes a multi-sensor data fusion module communicably coupled to the data acquisition and preprocessing module, the module being configured to apply hybrid fusion techniques. The system also includes an artificial intelligence and machine learning-based fault detection and diagnosis module operatively linked to the multi-sensor data fusion module, the AI/ML-based fault detection and diagnosis module configured to analyze fused data, detect anomalies, classify fault types, and determine a corresponding fault location within the microgrid. The system also includes an alert generation and reporting module operatively connected to the fault detection and diagnosis module, the module being configured to prioritize alerts according to severity and predictive failure timelines, and to generate real-time diagnostic notifications and maintenance recommendations through multi-channel communication to operator interfaces. The system also includes a cloud computing architecture configured to provide distributed processing, wherein one or more edge devices within a local network execute real-time analytics. The system also includes a control and intervention module operatively coupled to the alert generation and reporting module, the control and intervention module being configured to facilitate automated corrective actions or operator-initiated interventions, and further configured to provide closed-loop adaptive control by feeding execution outcomes back to the fault detection and diagnosis module and updating subsequent detection, classification, and intervention strategies.
[0022] In one embodiment, a continuous monitoring and adaptive learning unit operatively integrated with the AI/ML-based fault detection and diagnosis module, the continuous monitoring and adaptive learning unit configured to dynamically update fault detection models based on incoming sensor data, evolving microgrid conditions, and performance outcomes.
[0023] In one embodiment, the system is configured for early anomaly detection using predictive analytics, and is further adapted to predict potential failures by employing one or more techniques including time-series forecasting, statistical pattern recognition, and degradation modeling.
[0024] In one embodiment, the sensor module comprises a plurality of heterogeneous sensors operatively interfaced via plug-and-play connections employing communication protocols selected from Modbus, Zigbee, or MQTT, and further integrated into a centralized data acquisition unit configured to facilitate real-time monitoring and analysis of microgrid parameters.
[0025] In one embodiment, the data acquisition and preprocessing module is configured to execute preprocessing operations comprising adaptive noise filtering, temporal-spatial normalization, and context-driven feature extraction, the preprocessing being dynamically adjusted in response to variations in operating modes of the microgrid.
[0026] In one embodiment, the multi-sensor data fusion module is configured to employ hybrid computational techniques including kalman filtering, bayesian inference, and neural-network-driven ensemble learning, and is further adapted to perform real-time cross-validation of signals from the plurality of sensors by correlating multi-domain signatures across electrical, thermal, and mechanical data streams to extract actionable fault-specific features.
[0027] In one embodiment, the AI/ML-based fault detection and diagnosis module (140) is configured to automatically classify detected faults into categories based on severity, predicted failure timelines, and grid stability impact, and is further adapted to prioritize corrective actions in accordance with available system resources and operator-defined policies.
[0028] In one embodiment, the alert generation and reporting module is configured to display diagnostic results, system health status and actionable insights on an operator-accessible dashboard, and is further configured to generate real-time alerts and provide recommendations for preventive maintenance.
[0029] In one embodiment, the cloud computing architecture employs bandwidth-optimized communication by performing local edge summarization and transmitting compressed data formats to the cloud layer, thereby enabling reliable operation in rural or remote deployments with intermittent or low-speed network connectivity.
[0030] In the light of the above, in one aspect of the present disclosure, a method for early fault detection, classification, and predictive maintenance in a hybrid renewable energy source (RES) microgrid, is disclosed herein. The method includes deploying a plurality of sensors across renewable energy generation units, energy storage systems, and power conditioning systems of the hybrid RES microgrid to monitor operational and environmental parameters. The method also includes collecting and preprocessing sensor data by performing filtering, normalization, and handling of missing or corrupted values to generate processed data. The method also includes performing multi-sensor data fusion on the processed data using statistical or machine learning techniques to reduce noise, and provide a unified dataset. The method also includes analyzing the fused data by applying artificial intelligence or machine learning models to detect abnormal patterns, identify faults, and classify fault types and locations within the hybrid RES microgrid. The method also includes generating fault alerts and diagnostic reports comprising fault type, fault location, and severity level for transmission to a operator interface. The method also includes initiating corrective or preventive maintenance actions based on the generated alerts and reports, wherein the actions are performed by a human operator or an automated control module. The method also includes continuously updating and refining the machine learning models using feedback from detected faults, corrective actions, and system performance.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1A illustrates a schematic block diagram of the system, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 1B illustrates the block diagram representation of the system architecture, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 2A illustrates a step by step operation workflow of the system, in accordance with an exemplary embodiment of the present disclosure.
[0039] Fig. 2B a flowchart representation depicting implementation and operational workflow of the system, in accordance with an exemplary embodiment of the present disclosure.
[0040] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0041] The multi-sensor fusion-based system and method for early fault detection, classification, and predictive maintenance in hybrid renewable energy source (RES) microgridis illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0042] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0043] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0044] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0045] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0046] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0047] The present disclosure relates to a system and method for fault detection and predictive maintenance therein through a multi-sensor integration and fusion architecture. The system is configured to provide a comprehensive view of microgrid operations by aggregating data from a plurality of sensors, including but not limited to electrical measurement sensors, machine condition monitoring sensors, and ambient environment sensors. By utilizing modern artificial intelligence (AI) and machine learning (ML) approaches, the system enhances the accuracy of real-time anomaly detection, fault classification, and predictive forecasting, while simultaneously reducing false positives. The system is further designed for implementation across both edge-level devices and cloud infrastructures, thereby facilitating distributed, low-latency analytics alongside deep, long-term system modeling. Integration of multi-source sensor data significantly improves fault detection capabilities as compared to reliance on single-sensor systems. Moreover, the system incorporates adaptive learning mechanisms that dynamically adjust to evolving grid conditions and performance characteristics.
[0048] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of present disclosure. FIG. 1A illustrates a schematic block diagram of the system, in accordance with an exemplary embodiment of the present disclosure;
[0049] The system 100 for early fault detection, classification, and predictive maintenance in a hybrid renewable energy source (RES) microgrid. The system 100 is architected to integrate a plurality of functional modules, each operatively coupled to ensure seamless data acquisition, continuous monitoring, advanced analytics, and corrective interventions, improving operational efficiency and resilience of the microgrid. The system 100 is to address the unpredictability, and fault-prone nature of renewable energy units by enabling early anomaly detection, accurate fault classification, and predictive maintenance. Additionally, the system 100 minimizes downtime, prevents cascading failures, optimizes energy utilization, and extends the lifespan of microgrid assets. The system 100 further enhances decision-making by providing real-time actionable insights to operators while also supporting autonomous corrective actions through intelligent control modules.
[0050] In an exemplary embodiment, the system 100 may be embodied in a hybrid RES microgrid that includes multiple energy sources such as solar photovoltaic panels, wind turbines, battery storage systems, and power converters. The system 100 comprisesa sensor module 110, data acquisition and pre-processing module 120, multi-sensor fusion module 130, AI/ML based diagnosis module 140, alert generation and reporting module 150, cloud computing architecture 160, control and intervention module 170, and continuous monitoring and learning module 180. The system 100 provides a closed-loop intelligent framework that not only detects and diagnoses faults in real time but also actively learns from operational data to enhance predictive capability.
[0051] The system 100 may be includes a sensor module 110 is configured as a fundamental component of the system 100, adapted to enable continuous and real-time monitoring of operational parameters of the hybrid renewable energy source (RES) microgrid. The sensor module 110 comprises a plurality of heterogeneous sensors operatively coupled to various microgrid assets including, but not limited to solar panels, wind turbines, inverters, energy storage units, power converters, and grid interconnections. The sensor module 110 is further configured to interface with a centralized data acquisition system through wired or wireless communication protocols, thereby ensuring seamless data transfer for subsequent processing and analysis.
[0052] In an exemplary embodiment, the plurality of sensors 110 is strategically deployed across critical microgrid components to maximize diagnostic coverage and fault sensitivity. The plurality of sensors 110 are communicably linked and integrated into a centralized data acquisition system configured to receive, synchronize, and manage data streams from all sensors. The sensor module 110 thereby enables real-time collection of multi-domain data including electrical 111, thermal 113, vibrational 115, and environmental 117 parameters across the microgrid infrastructure.
[0053] The sensor module 110 facilitates the detection of a wide spectrum of anomalies including, but not limited to, irregular voltage or current fluctuations in inverters, overheating in batteries, mechanical wear in wind turbines, irradiance variability affecting solar panels, and abnormal grid interconnection states. By virtue of its heterogeneous deployment and centralized integration, the sensor module 110 ensures comprehensive situational awareness and forms the foundational layer for reliable multi-sensor data fusion, anomaly detection, and predictive maintenance within the system 100.
[0054] The data acquisition and preprocessing module 120 is operatively coupled to the sensor module 110 and is configured to serve as the intermediary processing unit responsible for aggregating, conditioning, and preparing sensor data for subsequent fusion and diagnostic analysis. The module 120 comprises one or more data acquisition units, communication interfaces, preprocessing circuits, and storage elements, collectively adapted to ensure that raw sensor signals are converted into reliable, standardized datasets. The coupling between the module 120 and the plurality of sensors 110 may be embodied through protocols such as RS-485, Modbus, or Ethernet, or wireless communication standards including ZigBee, Wi-Fi, or LoRaWAN.
[0055] The data acquisition and preprocessing module 120 may be implemented in hardware, software, or a hybrid thereof. In hardware embodiments, the module 120 may employ analog-to-digital converters (ADCs), signal conditioning circuits, and microcontrollers for initial processing. In web embodiments, the module 120 may comprise firmware or middleware running on embedded processors, programmable logic controllers (PLCs), or dedicated edge devices. The preprocessing module 120 may further incorporate memory elements including RAM, flash storage, or cloud-linked buffers to temporarily store sensor streams prior to transmission.
[0056] In an exemplary embodiment, the data acquisition and preprocessing module 120 is configured to continuously collect raw sensor data in real-time from the plurality of heterogeneous sensors 110. The module 120 executes essential preprocessing tasks such as noise filtering (e.g., using low-pass filters, moving averages, or wavelet-based denoising), normalization and data validation (detecting and correcting outliers, missing readings, or corrupted packets). The module 120 may also implement time synchronization mechanisms, ensuring that multi-sensor data streams are temporally aligned for accurate correlation during the data fusion stage. The data acquisition and preprocessing module 120 establishes a robust foundation for subsequent analytical modules, ensuring that only high-quality, standardized, and temporally aligned data is passed forward to the multi-sensor data fusion module 130.
[0057] The multi-sensor data fusion module 130 is communicably coupled to the data acquisition and preprocessing module 120 and is configured to integrate, correlate, and process heterogeneous sensor data in order to enhance fault detection accuracy and minimize the occurrence of false alarms. The module 130 may be deployed as a machine-learning or statistical modeling framework running on edge nodes, cloud servers, or distributed computing environments. The module may further include communication interfaces adapted to receive continuous pre-processed data streams from the data acquisition and preprocessing module 120.
[0058] The multi-sensor data fusion module 130 is configured to combine data obtained from the plurality of heterogeneous sensors 110, including, but not limited to, voltage sensors, current sensors, temperature sensors, vibration sensors, irradiance sensors, humidity sensors, and wind sensors, which are strategically deployed across solar panels, wind turbines, inverters, batteries, and grid connection points within the microgrid. The fusion 130 process employs computational techniques such as kalman filtering, bayesian inference, machine learning models, or deep learning-based sensor fusion frameworks.
[0059] The module 130 further incorporates correlation mechanisms to temporally and spatially align sensor data streams, enabling accurate cross-validation between multiple sensing modalities. By correlating patterns across electrical, thermal, mechanical, and environmental domains, the fusion process strengthens the reliability of fault detection, reduces false positives, and mitigates the risks of spurious alarms triggered by isolated sensor anomalies. In an exemplary embodiment, the multi-sensor data fusion module 130 may fuse real-time data originating from diverse sources such as voltage, current, temperature, vibration 115, and environmental sensors 117. The disclosed fused dataset is then transmitted to the AI/ML-based fault detection and diagnosis module 140 for higher-level anomaly detection, classification, and severity assessment. The multi-sensor data fusion module 130 significantly improves the accuracy and reliability of microgrid fault identification by utilizing complementary strengths of heterogeneous sensors.
[0060] The artificial intelligence and machine learning (AI/ML)-based fault detection and diagnosis module 140 is operatively linked to the multi-sensor data fusion module 130 and is configured to perform advanced analytics on the fused multi-sensor dataset to identify, classify, and localize faults within the hybrid renewable energy source (RES) microgrid. The module 140 serves as an intelligent computational engine that leverages state-of-the-art artificial intelligence and machine learning models to transform heterogeneous sensor 110 inputs into actionable diagnostic insights.
[0061] The module 140 is configured to process the fused dataset received from the multi-sensor data fusion module 130 and apply one or more computational approaches selected from, but not limited to, neural networks, support vector machines (SVMs), random forests, gradient boosting models, clustering algorithms, or deep learning frameworks. The module 140 further employs outlier detection techniques such as statistical anomaly detection, isolation forests, or probabilistic inference models to identify early false trends, thereby ensuring timely recognition of incipient faults before escalation.
[0062] The module 140 is further configured to perform fault classification, wherein detected anomalies are categorized into specific fault types, such as electrical faults, thermal overloads, mechanical vibrations, inverter malfunctions, or environmental parameter deviations. The module 140 also determines a corresponding fault location within the microgrid by mapping fault signatures to specific assets including solar panels, wind turbines, inverters, battery storage units, or grid interconnections. In an exemplary embodiment, the AI/ML-based fault detection and diagnosis module 140 additionally computes severity ratings for identified faults, thereby enabling prioritized maintenance actions. The module 140 may also incorporate adaptive learning techniques, whereby detection models are continuously updated and retrained based on new operational data and corrective action feedback, thus improving diagnostic accuracy over time. The AI/ML-based fault detection and diagnosis module 140 significantly enhances the scalability and adaptability of the system 100 by reducing false alarms, enabling early-stage fault detection, and providing precise fault categorization and localization.
[0063] The alert generation and reporting module 150 is operatively connected to the AI/ML-based fault detection and diagnosis module 140 and is configured to transform diagnostic outcomes into real-time notifications, actionable insights, and operator-accessible reports. The module 150 serves as the principal communication interface between the system 100 and human operators, thereby facilitating timely situational awareness and informed decision-making for microgrid management. The module may include embedded controllers, display panels, or operator consoles integrated within a control center. The module may also comprise graphical user interface (GUI) applications, dashboard frameworks, or mobile/web-based applications operatively deployed on operator terminals, portable devices, or cloud platforms. The module may further include secure communication protocols to ensure authenticated data transmission to authorized users.
[0064] The alert generation and reporting module 150 is configured to receive processed outputs from the AI/ML-based fault detection and diagnosis module 140 and generate real-time alerts whenever anomalies, faults, or deviations from normal operating conditions are detected. The alerts may be communicated via multiple channels, including visual dashboard notifications, SMS, email, or push notifications, without limitation. The module 150 further provides diagnostic results, system health status, and actionable insights through interactive dashboards accessible to operators. The dashboards may present information such as fault classification, severity levels, affected components, recommended corrective actions, and predictive maintenance schedules. In certain embodiments, the dashboards may also display trend analytics, historical performance data, and predictive indicators to assist in long-term planning.
[0065] In an exemplary embodiment, the alert generation and reporting module 150 is further configured to issue recommendations for preventive maintenance based on predictive insights generated by the system 100. Such recommendations may include scheduling of inspections or operational adjustments to mitigate future risks. The module 150 may additionally maintain a historical log of alerts and recommendations for subsequent analysis, regulatory compliance, and continuous model training. The alert generation and reporting module 150 ensures that system 100 operators receive timely, accurate, and actionable feedback regarding the operational state of the hybrid renewable energy source microgrid.
[0066] The cloud computing architecture 160 is operatively integrated within the system 100 and is configured to provide a hybrid framework of distributed processing, wherein computational tasks are selectively allocated between edge devices deployed within a local network and cloud servers deployed on remote or centralized infrastructures. The architecture 160 ensures a balance between real-time responsiveness and computationally intensive analysis, thereby enabling reliable and scalable operation of the hybrid renewable energy source microgrid.
[0067] In exemplary embodiments, the cloud computing architecture 160 may include one or more edge devices such as local controllers, embedded processors, or microgrid supervisory units disposed in proximity to the renewable energy generation units. The said edge devices are configured to execute real-time analytics, including noise filtering, data normalization, preliminary fault detection, and feature extraction. Such edge-level processing ensures low-latency decision-making, which is critical for identifying and responding to urgent or time-sensitive faults within the microgrid infrastructure. The cloud computing architecture 160 further incorporates remote cloud servers that are configured to perform advanced computational tasks, including but not limited to, pattern recognition, predictive modeling, long-term trend forecasting, anomaly correlation, and system-wide optimization. The said servers are also configured to handle long-term data storage, thereby enabling historical analysis and continuous improvement of AI/ML models utilized by the system 100.
[0068] Communication between the edge devices and cloud servers is facilitated through a secure and high-speed communication layer, employing encrypted protocols, authentication mechanisms, and redundancy safeguards to ensure confidentiality, and availability of transmitted data.
[0069] In certain embodiments, the cloud computing architecture 160 may implement a tiered processing framework, wherein an immediate anomaly detection and mitigation functions are executed locally at the edge, and a deeper diagnostic analysis and system 100 optimization functions are executed remotely in the cloud. This division of processing tasks advantageously enables the system to deliver real-time responses for critical fault conditions, while still utilizing high-performance cloud resources for advanced analytics that are non-urgent but computationally demanding. The cloud computing architecture 160 may support scalability and interoperability, enabling integration with multiple microgrids, external monitoring systems, and third-party diagnostic tools.
[0070] The control and intervention module 170 is operatively and communicably coupled to the alert generation and reporting module 150 and is configured to facilitate execution of both automated corrective actions and operator-initiated interventions within the hybrid renewable energy source microgrid. The module 170 functions as an active decision-execution layer, ensuring that anomalies, faults, or abnormal operating conditions identified by the preceding diagnostic modules are not only reported but also remedied through corrective operational responses. In exemplary embodiments, the control and intervention module 170 provides an operator interface through which authorized human operators may override, supplement, or modify automated responses. Such operator-initiated interventions may include manual fault isolation, recalibration of operational thresholds, or scheduling of preventive maintenance activities.
[0071] The control and intervention module 170 is further configured to employ a feedback loop mechanism, wherein the outcomes of executed corrective actions are monitored, analyzed, and relayed back into the system 100 for continuous performance optimization. In certain embodiments, the module 170 may incorporate fail-safe mechanisms and redundancy protocols to ensure that critical corrective actions are reliably executed even in the event of partial system failures or communication disruptions. The control and intervention module 170 provides a comprehensive framework for both autonomous and operator-directed corrective action, coupled with adaptive learning from prior interventions, thereby ensuring improved operational resilience, reduced downtime, and enhanced efficiency of the microgrid system 100.
[0072] The continuous monitoring and adaptive learning unit 180 is operatively integrated with the AI/ML-based fault detection and diagnosis module 140 and is configured to provide a self-evolving intelligence layer that enhances the long-term performance of the system 100. The unit 180 operates to continuously acquire, assess, and analyze real-time operational data from the plurality of sensors 110, as well as outcomes generated by the multi-sensor data fusion module 130, fault detection and diagnosis module 140, and control and intervention module 170.
[0073] In exemplary embodiments, the continuous monitoring and adaptive learning unit 180 is configured to dynamically update and retrain fault detection models based on incoming sensor data streams, evolving microgrid operating conditions, and historical system performance outcomes. The adaptive learning framework ensures that predictive models remain aligned with non-stationary behaviors, seasonal variations, equipment aging, and unforeseen system disturbances. Furthermore, the unit 180 may also archive historical data and learning outcomes into the cloud computing architecture 160, thereby enabling long-term trend analysis. The continuous monitoring and adaptive learning unit 180 provides an intelligent self-optimizing framework that ensures early anomaly detection, adaptive model accuracy, and sustainable predictive maintenance strategies, thereby enhancing the overall resilience, and operational reliability of the hybrid RES microgrid system 100.
[0074] In an alternative embodiment, as illustrated in FIG. 1B, shown a block diagram representation of the system 100 architecture, substantially corresponding to the system 100 described with reference to FIG. 1A, but presented in a schematic block-level format to highlight the interconnection and functional coupling between the plurality of modules.
[0075] The system 100 comprises a multi-sensor layer 110 configured to acquire heterogeneous data in real time from a plurality of sensors. The multi-sensor layer 110 is operatively coupled to a data acquisition and preprocessing module 120 configured to perform preliminary operations including noise filtering, data cleaning, and normalization, thereby ensuring accuracy, consistency, and readiness of the acquired data.
[0076] The preprocessed data is transmitted to a multi-sensor fusion engine 130 configured to integrate and correlate heterogeneous signals using techniques such as kalman filtering, bayesian inference, and machine learning-based fusion for robust cross-validation and enhanced diagnostic reliability. The fused data is processed by a fault diagnosis module 140 employing artificial intelligence (AI), machine learning (ML), and statistical outlier detection to perform anomaly detection, fault classification, severity determination, and subsystem-level fault localization.
[0077] A communication and edge-cloud hybrid infrastructure 160 is operatively linked to the fault diagnosis module 140 to enable distributed processing, wherein edge devices execute low-latency analytics proximate to the microgrid and cloud servers perform deep analytics, predictive modeling, and long-term data storage.
[0078] A user interface and alert system 150 is communicably coupled to the infrastructure 160, the interface being configured to display diagnostic results, system health status, and predictive insights via operator-accessible dashboards, and further to generate real-time alerts, notifications, and maintenance recommendations.
[0079] FIG 2A illustrates a flowchart representing the stepwise operational method 200 of the disclosed system 100, in accordance with an exemplary embodiment of the present disclosure. The method 200 commences at step 201.
[0080] The first step 201 of the method 200 involves deploying a plurality of sensors 110 across renewable energy generation units, energy storage systems, and power conditioning systems of the hybrid RES microgrid, wherein said sensors 110 are adapted to measure operational and environmental parameters including, but not limited to, voltage, current, temperature, vibration, and ambient conditions. The plurality of sensors 110 is operatively coupled to a centralized data acquisition unit configured to capture and transmit data streams in real time. In certain embodiments, the deployment further includes calibrating each sensor against reference standards, assigning network addresses, performing secure enrolment, synchronizing time sources (such as PTP/NTP) to enable coherent time-stamping, verifying wire or wireless connectivity, and registering all devices with the data acquisition unit, thereby ensuring accurate and reliable data capture.
[0081] The next step 203 of the method 200 involves collecting and preprocessing sensor data by performing filtering, normalization, and handling of missing or corrupted values to generate processed data. It ensures conversion of raw, heterogeneous signals into consistent, high-quality input data suitable for downstream analytics.
[0082] The next step 205 of the method 200 involves performing multi-sensor data fusion on the processed data using statistical or machine learning techniques to reduce noise, and provide a unified dataset. Said fusion integrates and correlates heterogeneous signals across electrical, thermal, mechanical, and environmental channels. It enables improved robustness of anomaly detection, reduces false positives and false negatives, and enhances confidence in diagnostic outcomes.
[0083] The next step 207 of the method 200 involves analyzing the fused data by applying artificial intelligence or machine learning models to detect abnormal patterns, identify faults, and classify fault types and locations within the hybrid RES microgrid. The analysis facilitates automated and real-time recognition of both incipient and manifest failures, thereby ensuring faster response, severity-based prioritization, and improved operational reliability. In certain embodiments, the analysis includes statistical or model-based outlier detection, supervised learning models such as neural networks, support vector machines, or gradient boosting algorithms, unsupervised clustering for detection of novel fault signatures, estimation of fault severity indices, and mapping of diagnostic outcomes to specific microgrid assets including solar arrays, inverters, wind turbines, batteries, and interconnection points.
[0084] The next step 209 of the method 200 involves generating fault alerts and diagnostic reports comprising fault type, fault location, and severity level for transmission to an operator interface. The generated reports deliver actionable intelligence to human operators and automated control systems. In certain embodiments, the said step further comprises rendering dashboards with graphical trend analyses and health summaries, appending recommended corrective actions, logging diagnostic events for compliance or audit purposes, and issuing real-time notifications through visual, email, SMS, or push-based channels with acknowledgment tracking mechanisms.
[0085] The next step 211 of the method 200 involves initiating corrective or preventive maintenance actions based on the generated alerts and reports, wherein the actions are performed by a human operator or an automated control module. It provides a prevention of fault escalation, reduction of equipment damage, and enhanced continuity of service. It mitigates escalation of faults, reduces risk of component failure, ensures continuity of service, and enhances overall operational safety of the hybrid RES microgrid.
[0086] The final step 213 of the method 200 involves continuously updating and refining the machine learning models using feedback from detected faults, corrective actions, and system performance. The adaptive learning enables the models to dynamically adjust to evolving microgrid operating conditions, variations in load profiles, and progressive equipment aging, thereby ensuring sustained accuracy, and robustness of fault detection and diagnostic outcomes over time.
[0087] In an alternative embodiment, as illustrated in Fig. 2B. The operational sequence is initiated with sensor deployment and integration, wherein a plurality of heterogeneous sensors 110 is strategically positioned across various components of the microgrid. Subsequently, the process transitions to data collection and preprocessing, wherein raw sensor data is continuously gathered and subjected to preprocessing techniques.
[0088] Following preprocessing, the system 100 executes multi-sensor data fusion 130, wherein heterogeneous data streams are integrated utilizing fusion algorithms, including but not limited to kalman filtering, bayesian inference, or machine learning-based fusion models.
[0089] Thereafter, the workflow advances to fault detection and diagnosis 140, wherein the fused dataset is analyzed through the application of artificial intelligence and machine learning models.
[0090] Upon detection and diagnosis of a fault, the process proceeds to alert generation and reporting 150, wherein automated alerts and notifications are generated and communicated to system operators. Responsive to such alerts, the system 100 enables operator intervention and maintenance 170, wherein corrective measures are executed by human operators or, alternatively, by automated maintenance modules.
[0091] Finally, the system 100 sustains continuous monitoring and learning 180, wherein ongoing real-time sensor monitoring is maintained, and AI/ML-based detection models 140 are adaptively updated in accordance with new operational data and evolving system behaviour.
[0092] The present disclosure relates to a system 100 and method 200 for early fault detection, classification, and predictive maintenance in hybrid renewable energy source (RES) based microgrids. The system 100 employs a hybrid edge–cloud computing architecture, wherein initial operations such as noise filtering, feature extraction, and local anomaly detection are executed at the edge to enable low-latency responses for critical faults, while advanced operations such as statistical pattern recognition, trend forecasting, and degradation modeling are performed in the cloud to support long-term diagnostics and strategic decision-making. The system 100 generates a health profile of the microgrid by integrating heterogeneous sensor data 110 across electrical, thermal, mechanical, and environmental domains, thereby enabling cross-domain correlation for enhanced root-cause analysis. The system 100 further incorporates adaptive learning through a continuously updated fault-signature database, enabling recognition of recurring and evolving fault patterns, including complex or previously unseen anomalies. Faults are automatically classified based on severity, impact on power quality, and system safety, thereby allowing prioritization of critical issues and scheduling of non-critical events to optimize maintenance and resource allocation. The modular sensor framework, supporting standardized communication protocols such as Modbus, Zigbee, and MQTT, allows plug-and-play deployment across diverse microgrid topologies and enables scalable system 100 expansion. The system 100 is further equipped with built-in anomaly detection mechanisms for identifying irregular communication patterns or falsified sensor data, thereby mitigating cyber-physical attacks and ensuring resilience of operations in critical infrastructures.
[0093] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0094] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0095] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0096] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0097] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A system (100) for early fault detection, classification, and predictive maintenance in a hybrid renewable energy source (RES) microgrid, comprising:
a sensor module (110)comprising a plurality of heterogeneous sensors operatively deployed across renewable energy generation units, module (110) is configured to continuously monitor one or more parameters of the microgrids;
a data acquisition and preprocessing module (120) operatively coupled to the sensor module 110, the data acquisition and preprocessing module (120) configured to collect sensor data, filter noise, normalize values, and handle missing or incomplete data;
a multi-sensor data fusion module (130) communicably coupled to the data acquisition and preprocessing module (120), the module (130) being configured to apply hybrid fusion techniques;
an artificial intelligence and machine learning-based fault detection and diagnosis module (140) operatively linked to the multi-sensor data fusion module (130), the AI/ML-based fault detection and diagnosis module (140) configured to analyze fused data, detect anomalies, classify fault types, and determine a corresponding fault location within the microgrid;
an alert generation and reporting module (150) operatively connected to the fault detection and diagnosis module (140), the module (150) being configured to prioritize alerts according to severity and predictive failure timelines, and to generate real-time diagnostic notifications and maintenance recommendations through multi-channel communication to operator interfaces;
a cloud computing architecture (160) configured to provide distributed processing, wherein one or more edge devices within a local network execute real-time analytics;
a control and intervention module (170) operatively coupled to the alert generation and reporting module (150), the control and intervention module (170) being configured to facilitate automated corrective actions or operator-initiated interventions, and further configured to provide closed-loop adaptive control by feeding execution outcomes back to the fault detection and diagnosis module (140) and updating subsequent detection, classification, and intervention strategies.
2. The system (100) as claimed in claim 1, wherein a continuous monitoring and adaptive learning unit (180) operatively integrated with the AI/ML-based fault detection and diagnosis module (140), the continuous monitoring and adaptive learning unit (180) configured to dynamically update fault detection models based on incoming sensor data, evolving microgrid conditions, and performance outcomes.
3. The system (100) as claimed in claim 1, wherein the system (100) is configured for early anomaly detection using predictive analytics, and is further adapted to predict potential failures by employing one or more techniquesincluding time-series forecasting, statistical pattern recognition, and degradation modeling,
4. The system (100) as claimed in claim 1, wherein the sensor module (110) comprises a plurality of heterogeneous sensors operatively interfaced via plug-and-play connections employing communication protocols selected from Modbus, Zigbee, or MQTT, and further integrated into a centralized data acquisition unit configured to facilitate real-time monitoring and analysis of microgrid parameters.
5. Thesystem (100) as claimed in claim 1, wherein the data acquisition and preprocessing module (120) is configured to execute preprocessing operations comprising adaptive noise filtering, temporal-spatial normalization, and context-driven feature extraction, the preprocessing being dynamically adjusted in response to variations in operating modes of the microgrid.
6. The system (100) as claimed in claim 1, wherein the multi-sensor data fusion module (130) is configured to employ hybrid computational techniques including kalman filtering, bayesian inference, and neural-network-driven ensemble learning, and is further adapted to perform real-time cross-validation of signals from the plurality of sensors (110) by correlating multi-domain signatures across electrical, thermal, and mechanical data streams to extract actionable fault-specific features.
7. The system (100) as claimed in claim 1, wherein the AI/ML-based fault detection and diagnosis module (140) is configured to automatically classify detected faults into categories based on severity, predicted failure timelines, and grid stability impact, and is further adapted to prioritize corrective actions in accordance with available system resources and operator-defined policies.
8. The system (100) as claimed in claim 1, wherein the alert generation and reporting module (150) is configured to display diagnostic results, system health status and actionable insights on an operator-accessible dashboard, and is further configured to generate real-time alerts and provide recommendations for preventive maintenance.
9. The system (100) as claimed in claim 1, wherein the cloud computing architecture (160) employs bandwidth-optimized communication by performing local edge summarization and transmitting compressed data formats to the cloud layer, thereby enabling reliable operation in rural or remote deployments with intermittent or low-speed network connectivity.
10. A method (200) for early fault detection, classification, and predictive maintenance in a hybrid renewable energy source (RES) microgrid, comprising the steps of:
deploying a plurality of sensors across renewable energy generation units, energy storage systems, and power conditioning systems of the hybrid RES microgrid to monitor operational and environmental parameters (201);
collecting and preprocessing sensor data by performing filtering, normalization, and handling of missing or corrupted values to generate processed data (203);
performing multi-sensor data fusion on the processed data using statistical or machine learning techniques to reduce noise, and provide a unified dataset (205);
analyzing the fused data by applying artificial intelligence or machine learning models to detect abnormal patterns, identify faults, and classify fault types and locations within the hybrid RES microgrid (207);
generating fault alerts and diagnostic reports comprising fault type, fault location, and severity level for transmission to a operator interface (209);
initiating corrective or preventive maintenance actions based on the generated alerts and reports, wherein the actions are performed by a human operator or an automated control module (211); and
continuously updating and refining the machine learning models using feedback from detected faults, corrective actions, and system performance (213).