Description:FIELD OF INVENTION
[0001] The present disclosure relates generally to the field of photovoltaic (PV) power conversion systems. More particularly, it pertains to a method and apparatus for maximum power point tracking (MPPT), and specifically to a module-level power electronics (MLPE) system designed for robust and efficient operation under varying environmental conditions, including partial and dynamic shading.
BACKGROUND OF INVENTION
[0002] The present disclosure relates generally to the field of photovoltaic (PV) power conversion systems. More particularly, it pertains to a method and apparatus for maximum power point tracking (MPPT), and specifically to a module-level power electronics (MLPE) system designed for robust and efficient operation under varying environmental conditions, including partial and dynamic shading.
[0003] The global demand for clean and resilient energy has accelerated the adoption of renewable energy sources, with photovoltaic (PV) systems being a leading technology. A primary objective in the operation of any PV system is to maximize the energy harvested from the solar panels. However, the power output of a PV panel is highly variable, depending on dynamic environmental factors such as solar irradiance and ambient temperature. To address this variability, PV systems universally employ power electronics interfaces, such as DC-DC converters, which utilize MPPT algorithms to continuously adjust the panel's operating point to the maximum power point (MPP), thereby maximizing energy yield.
[0004] A significant and persistent challenge in PV system operation is the condition of partial shading (PS). PS occurs when portions of a PV array are subjected to non-uniform irradiation due to fixed or moving obstacles such as buildings, trees, pillars, or passing clouds. When a solar panel is partially shaded, its internal bypass diodes may activate, creating a complex power-voltage (P-V) characteristic curve with multiple power peaks. These peaks consist of several local maximum power points (LMPPs) and a single, often non-obvious, global maximum power point (GMPP). This complex P-V landscape makes effective MPPT exceptionally difficult.
[0005] Conventional MPPT algorithms are ill-equipped to handle these complexities. Simple methods, such as the Constant Voltage (CV) algorithm, are fast but fail under partial shading. They attempt to force the panel to a nominal voltage, which can cause a catastrophic drop in current and a collapse in power generation when bypass diodes are active. More common iterative methods, such as Perturb & Observe (P&O) and Incremental Conductance (INC), are prone to becoming "trapped" at an LMPP, failing to find the true GMPP and thus leaving significant energy unharvested. Furthermore, the slow, iterative nature of these algorithms often interferes with the higher-level MPPT cycle of the main string inverter, which can degrade the performance of the entire solar array.
[0006] In an attempt to solve this problem, a number of advanced optimization algorithms have been proposed, including those based on particle swarm optimization (PSO), genetic algorithms, or other metaheuristic techniques. While these intelligent algorithms demonstrate a better ability to locate the GMPP, they suffer from critical practical limitations. They typically involve high computational complexity, require significant processing power and memory, and can have slow convergence times. These characteristics make them too costly and impractical for widespread implementation in cost-sensitive module-level power electronics (MLPE) devices.
[0007] Furthermore, some existing solutions that can achieve global optimization do so by requiring communication between all MLPE devices in a string. This approach creates a closed, proprietary ecosystem, limiting compatibility to specific non-MPPT inverters and preventing their use with the vast installed base of standard, independent string inverters. Consequently, there remains a pressing need in the art for an MPPT method that is not only effective at finding the GMPP under all shading conditions but is also fast, computationally efficient, and robust enough to be practically implemented at the module level without requiring inter-device communication.
[0008] Accordingly, the present invention provides a technical solution to the aforementioned limitations by disclosing an adaptive, self-learning method and apparatus for MPPT. The present invention overcomes the deficiencies of the prior art by providing a computationally efficient system that accurately identifies the specific shading condition and executes a tailored optimization strategy. It utilizes a rapid search mechanism to ensure fast convergence, thereby minimizing interference with string-level inverters, and employs strategic decision-making to avoid trapping in local maxima. By incorporating a self-learning capability based on historical operational data and operating independently at the module level, the present invention provides a practical, high-performance, and widely compatible solution for maximizing power generation under all environmental conditions.
OBJECT OF INVENTION
[0009] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
[0010] An object of the present invention is to overcome the disadvantages of the prior art by providing an improved, adaptive, and self-learning method and apparatus for maximum power point tracking (MPPT) that maximizes the energy yield of a photovoltaic (PV) system under all operating conditions.
[0011] Another object of the present invention to provide a method that can accurately identify different shading conditions, including no shadow, mild/diffuse shadow, and hard shadow, by analyzing the characteristics of voltage shifts on the solar panel.
[0012] A further object of the present invention is to provide a method that intelligently selects an optimal power generation strategy based on the identified shading condition, rather than defaulting to a single, often inefficient, tracking mode.
[0013] Another object of the present invention is to provide a method that, under hard shading conditions, assesses and selects the most powerful operating state, whether it is operating at a lower voltage corresponding to an activated bypass diode or at a nominal, unshaded MPPT voltage.
[0014] A further object of the present invention is to provide an MPPT algorithm that achieves rapid convergence on the optimal operating point, thereby minimizing operational interference with the higher-level MPPT cycle of a standard string inverter.
[0015] Yet another object of the present invention is to provide a robust and stable search algorithm, specifically a rapid search, that includes an intelligent reversion mechanism to prevent instability and oscillations while determining the optimal operating point.
[0016] Another object of the present invention is to provide a truly adaptive and self-learning system that stores historical operational data and utilizes it to determine an optimized starting point for future MPPT operations, thereby reducing convergence time and improving efficiency.
[0017] A further object of the present invention is to ensure a seamless and rapid recovery to the nominal, maximum power operating range upon the removal of a shading condition.
[0018] Another object of the present invention is to provide a solution that enhances the operational lifetime and reliability of solar panels by minimizing the creation of hotspots associated with prolonged or inefficient operation under partial shading.
[0019] A still further object of the present invention to provide a cost-effective and widely compatible apparatus, in the form of a module-level power electronics (MLPE) device, that embodies the aforementioned methods and operates independently without requiring communication with other devices, ensuring compatibility with standard PV system components.
[0020] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY OF THE INVENTION
[0021] The present invention provides a method and an apparatus for adaptively optimizing the maximum power point tracking (MPPT) of a solar panel, particularly to overcome the inefficiencies caused by varying and partial shading conditions. The invention solves the technical problem of conventional MPPT algorithms which are often slow, computationally expensive, or prone to getting trapped in local power maxima, thereby failing to harvest the maximum available energy.
[0022] The method of the present invention involves a multi-stage intelligent process. Initially, the system monitors the voltage of the solar panel to detect and characterize any voltage shifts. It accurately identifies the nature of the shading condition by distinguishing between gradual voltage variations, which are typically caused by temperature changes or mild/diffuse shading, and abrupt, significant voltage drops that indicate a hard shading event and the activation of the panel's internal bypass diodes.
[0023] Based on this precise identification, the system selects an optimal power optimization strategy from a plurality of predefined strategies. Each strategy corresponds to a specific operational state of the panel, such as different bypass diode activation states. For instance, under hard shading, the system intelligently assesses whether operating at a lower voltage with an active bypass diode is more power-efficient than forcing the panel to a nominal voltage. For milder conditions, it pursues the nominal MPPT voltage.
[0024] To execute the chosen strategy, the invention employs a rapid and robust search algorithm to quickly converge on the true optimal operating point within the selected voltage range. This search includes a novel stability feature, wherein it reverts to a previously known stable state if a search step induces a voltage drop beyond a predetermined threshold, thereby preventing system instability.
[0025] A central aspect of the invention is its self-learning capability. The system continuously stores key operational data, including observed voltage, current, and duty cycle values, in a database. This historical data is then utilized to determine an optimized and highly accurate starting point for subsequent MPPT operations, which significantly reduces convergence time and enhances the system's adaptive response to recurring shading patterns.
[0026] The present invention also provides for an apparatus, typically embodied as a module-level power electronics (MLPE) device, comprising a processor and a memory configured to execute the steps of the aforementioned method. The apparatus is designed to operate independently and without requiring communication with other devices, ensuring broad compatibility with existing solar infrastructure.
[0027] The proposed invention, therefore, offers a significant technical advancement over the prior art by providing an MPPT solution that is fast, accurate, computationally efficient, and robustly avoids local maxima, ensuring the highest possible energy yield under all environmental conditions.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0028] The other objects, features and advantages will occur to those skilled in the art from the following description of the preferred embodiment and the accompanying drawings in which:
[0029] Figure 1 is a schematic diagram illustrating an exemplary hardware environment and system architecture for implementing the method and apparatus according to an embodiment of the present invention.
[0030] Figure 2 is a flowchart illustrating a specific embodiment of the overall adaptive MPPT algorithm, according to an embodiment of the present invention.
[0031] Figure 3 is a flowchart detailing the inventive rapid search algorithm according to an embodiment of the present invention.
[0032] Figure 4 is a set of time-series plots illustrating the performance of a conventional Constant Voltage MPPT algorithm under a hard shadow condition, showing changes in panel voltage, duty cycle, power, and current over time.
[0033] Figure 5A is a set of time-series plots illustrating the performance of a control solar panel without an optimizer under a hard shadow condition, showing panel voltage, power, and current.
[0034] Figure 5B is a set of time-series plots illustrating the performance of a solar panel equipped with the adaptive, self-learning optimizer of the present invention under the same hard shadow condition as Figure 5A, for the purpose of direct comparison.
DETAILED DESCRIPTION OF THE INVENTION
[0035] The present invention may be embodied in several forms, and the details of embodiments of the present invention will be described in the following content with figures. The embodiments described below with reference to the drawings are merely illustrative of the technical solutions of the present disclosure but are not to be construed as limited to the technical solutions of the present disclosure.
[0036] 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 the present invention is provided for illustration purposes only and not for the purpose of limiting the invention as defined by the appended claims. As used in the description of the invention and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
[0037] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
[0038] According to an exemplary embodiment of the present invention, a method for optimizing maximum power point tracking (MPPT) of a solar panel, to be performed by a processor, is provided. The method comprises the steps of: monitoring a voltage of the solar panel to detect a voltage shift; identifying a shading condition of the solar panel based on characteristics of the detected voltage shift; selecting, based on the identified shading condition, one of a plurality of power optimization strategies, wherein each said strategy corresponds to a different operational state of the solar panel; executing the selected power optimization strategy to determine an optimal operating point for the solar panel; and storing operational data associated with the optimal operating point in a database to facilitate future MPPT operations.
[0039] In an embodiment, the step of identifying the shading condition comprises distinguishing between a gradual voltage shift within a first predetermined range of approximately 5-10% of the panel voltage, which is associated with temperature changes or mild/diffuse shading, and an abrupt voltage shift within a second, larger predetermined range of approximately 15% or more of the panel voltage, which is associated with a bypass diode activation condition.
[0040] In a further embodiment, for the hard shading condition, the selected optimization strategy comprises operating the solar panel at a lower voltage and a higher current corresponding to an activated bypass diode of a shaded portion of the solar panel when said operation yields a higher total power output than operating at a nominal MPPT voltage. Conversely, for the mild/diffuse shading or a temperature-driven voltage shift condition, the selected optimization strategy comprises operating the solar panel at a nominal MPPT voltage.
[0041] In a still further embodiment, executing the selected power optimization strategy comprises performing a rapid search that converges to an optimal operating point within a predetermined error margin of approximately 5% in five or fewer iterations. Said search further comprises the inventive step of reverting to a previous duty cycle if a resulting panel voltage shifts below a previous voltage by more than a predetermined threshold, thereby ensuring operational stability.
[0042] According to another exemplary embodiment, an apparatus (100) for optimizing MPPT of a solar panel (110) is provided. The apparatus (100) comprises a processor (105) and a memory (106) operatively coupled to the processor (105). The memory (106) stores instructions that, when executed by the processor (105), cause the apparatus (100) to perform the steps of the method as described in the preceding embodiments and claims.
[0043] Figure. 1 is a schematic diagram illustrating an exemplary hardware environment and system architecture for implementing the method and apparatus (100) of the present invention. The apparatus (100) is designed as a module-level power electronics (MLPE) device, providing individual optimization for a single solar panel (110).
[0044] The apparatus (100) comprises a solar panel (110), which serves as the DC power source. The positive and negative output terminals of the solar panel (110) are electrically connected to the input of a DC-DC Converter (101). The DC-DC Converter (101) is a power electronics interface whose primary function is to modulate the electrical load seen by the solar panel (110), thereby controlling the panel's operating point (its voltage and current). The output of the DC-DC Converter (101) is then directed "To Load (120)," which typically represents a connection to a series string of other similar MLPE devices, which in turn feeds into a main string inverter.
[0045] The core of the invention is embodied in the MPPT Controller (104), which corresponds to the apparatus (100) recited in claim 14. The MPPT Controller (104) comprises a Processor (105) and a Memory (106). The Memory (106) stores a set of executable instructions which, when executed by the Processor (105), cause the MPPT Controller (104) to perform the steps of the method recited in claims 1 through 13.
[0046] To enable the real-time operation of the claimed method, the apparatus (100) includes a Current Sensor (103) and a Voltage Sensor (102). The Current Sensor (103) is operatively connected to monitor the instantaneous current flowing from the solar panel (110). The Voltage Sensor (102) is operatively connected to monitor the instantaneous voltage across the terminals of the solar panel (110). Both sensors (105, 106) are configured to provide continuous, real-time data signals corresponding to the measured current and voltage to the MPPT Controller (104).
[0047] The operation of the system constitutes a closed-loop feedback mechanism, providing direct and enabling support for the claimed method. The process is as follows:
• Monitoring: The Current Sensor (103) and Voltage Sensor (102) continuously measure the panel's (110) output and feed these data signals to the MPPT Controller's Processor (105). This constitutes the step of "monitoring the voltage of the solar panel (110) to detect a voltage shift." The processor (105) calculates the instantaneous power by multiplying the received voltage and current values.
• Processing: The Processor (105) executes the instructions stored in its Memory (106). It analyzes the stream of incoming voltage data to identify the shading condition by distinguishing between gradual and abrupt shifts. Based on this identification, it selects the optimal power strategy from the plurality of strategies stored in memory (106), such as operating in a lower-voltage bin for hard shading or pursuing the nominal voltage for diffuse shading.
• Actuation and Control: Based on the selected strategy and the subsequent calculations of the rapid search, the Processor (105) generates a high-frequency Control Signal (107). This signal is typically a pulse-width modulation (PWM) signal (107). The Control Signal (107) is sent to the DC-DC Converter (101), where it drives the gate of an internal high-frequency switch.
• Execution and Repetition: By precisely modulating the duty cycle of the PWM Control Signal (107), the Processor (105) adjusts the switching behavior of the DC-DC Converter (101). This adjustment changes the effective impedance presented to the solar panel (110), thereby forcing the panel (110) to a new operating point. The sensors (102, 103) immediately measure the new voltage and current at this new point, and the data is fed back to the MPPT Controller (104), where the loop begins again. This continuous, high-speed loop allows the apparatus to dynamically and adaptively track the true maximum power point in real-time.
[0048] The Memory (106) within the MPPT Controller (104) serves a dual purpose. First, it stores the firmware, the set of executable instructions that define the entire method. Second, it contains a non-volatile section that functions as the database for the self-learning feature. When an optimal operating point is successfully determined, the Processor (105) writes a data tuple, comprising the shading condition identifier, the optimal voltage, current, and duty cycle to this database. This stored historical data is then utilized in future operations to provide a more accurate starting point for the MPPT search, thereby reducing convergence time and improving overall system efficiency.
[0049] Figure 2 is a flowchart illustrating a specific embodiment of the overall adaptive MPPT algorithm, which comprises:
• Step 1 (Recovery Mechanism): The process (105) begins at the primary decision block, which checks if Panel Voltage (VP) > Upper Voltage Limit (Vmax). This is the direct implementation of the feedback mechanism for detecting the deactivation of a bypass diode. A "Yes" condition signifies that a shadow has been removed, causing the panel's voltage to rise sharply above its nominal shaded range. The processor (105) executes the recovery step: it sets the duty cycle to 100% and resets internal flags. This action immediately restores the panel (110) to its nominal, unshaded operating mode, ensuring the seamless continuation of maximum energy generation without delay.
• Step 2 (Operating Bin Identification): If the panel (110) is not in recovery mode, the processor (105) identifies its current operating bin by comparing the measured voltage against predefined thresholds (e.g., 36V, 24V). These thresholds define the boundaries of the distinct MPPT operating bins. For example, a voltage between 24V and 36V corresponds to a first shaded operating bin (e.g., one bypass diode active), while a voltage below 24V corresponds to a second, more heavily shaded bin (e.g., two or more bypass diodes active).
• Step 3 (Strategy Execution): Once the operating bin is identified, the processor (105) executes the optimization strategy for that bin. It first calculates the current Power, Current, and Duty Cycle. It then sets a new duty cycle and, after a short delay (e.g., 3s), checks if the Voltage Increased?. A "Yes" triggers a refined search for the optimal current. A "No" triggers a reversion to a stable state. This logical flow demonstrates the execution of a selected power optimization strategy as per claim 1.
[0050] In one embodiment, the table below defines the characteristics of the MPPT operating bins. Each bin corresponds to a distinct voltage range and is associated with a specific bypass diode activation state, thereby enabling the plurality of power optimization strategies as claimed:
Bin Number Voltage Range (V)
Bin 1 0 ≤ V < 3
Bin 2 3 ≤ V < 18
Bin 3 18 ≤ V < 26
Bin 4 V ≥ 36
[0051] Each MPPT operating bin is configured for a unique set of power management actions, with the respective bypass diode activation tailored for optimal module performance in that voltage range. Wherein the plurality of power optimization strategies corresponds to distinct MPPT operating bins, and each bin is associated with a specific bypass diode activation state
[0052] Figure 3 is a flowchart detailing the inventive rapid search algorithm, which comprises:
• Step 301: Initialize Variables for Search. The processor (105) retrieves from memory (106) or calculates the initial search boundaries (e.g., min and max current) for the selected operating bin.
• Step 302: Calculate Mid Current and New Duty Cycle. The processor (105) calculates the midpoint of the current search range and then calculates the new duty cycle required to drive the converter to achieve this midpoint current.
• Step 303: Set New Duty Cycle, Get ADC. The processor (105) applies this new duty cycle via the PWM signal (107) and measures the resulting Panel Voltage (VP) from the voltage sensor (102) as Step 304.
• Step 305 & 306 (Stability Check): The processor (105) checks if the Panel Voltage (VP) has increased. If not, it performs the critical stability check: it determines if the Panel Voltage (VP) Drop > 0.8.
• Step 307 (Inventive Reversion): If the voltage drop exceeds the predetermined threshold ("Yes" at Step 306), the processor (105) executes the reversion mechanism. It immediately discards the unstable setting and reverts to the last known-stable duty cycle. This prevents oscillations and provides robust performance.
• Step 308 (Continuation): If the voltage is stable, the processor (105) updates the search boundaries and loops back to Step 302 to continue the search until convergence within the error margin is achieved.
[0053] According to a detailed embodiment, the apparatus (100) of the present invention is embodied as a standalone module-level power electronics (MLPE) device. The physical form factor of the apparatus is a compact, environmentally sealed enclosure, typically fabricated from a thermally conductive material such as cast aluminum to facilitate heat dissipation. The enclosure is designed to meet or exceed industry standards for outdoor electronic equipment, such as an IP67 or IP68 rating, ensuring its resilience to moisture, dust, and temperature extremes. The apparatus is designed for direct physical integration with a single solar panel. In a typical installation, the apparatus (100) is mounted either to the back frame of the solar panel (110) or to the mounting rail directly beneath it.
[0054] The electrical integration of the apparatus (100) is designed for simplicity and compatibility with standard installation practices, as illustrated in the hardware environment of Figure 1. The apparatus (100) is equipped with standard photovoltaic connectors, such as MC4-type connectors, for its input and output leads. The integration process for a string of solar panels (110) is as follows:
• Input Connection: The positive and negative output leads from a single solar panel (110) are connected directly to the corresponding input terminals of one MLPE apparatus. This creates a one-to-one pairing between each solar panel (110) and its dedicated MLPE device.
• String Formation: The output leads of the MLPE apparatus are then used to form the series string. The positive output lead of the first MLPE device is connected to the negative output lead of the second MLPE device. This series connection is repeated for all subsequent devices in the string, creating a daisy chain. The final positive terminal of the first MLPE device in the string and the final negative terminal of the last MLPE device in the string become the overall output terminals for the entire solar array string.
• Inverter Connection: These final string output terminals are then connected to the DC input of a standard string inverter.
[0055] The apparatus (100) can be configured either as a standalone module-level power electronics (MLPE) device that requires no data communication wires or control links between adjacent MLPE units, or as a device that includes communication links between MLPE units. In the standalone embodiment, all power transmission and control signaling occur exclusively through the DC power lines. This guarantees a "plug-and-play" installation process, which closely resembles the installation procedure for a standard solar array and thus minimizes both system complexity and cost. In the embodiment with communication, the MLPE units are equipped to communicate using power line communication, wireless mesh, or other suitable interfaces. This feature enables advanced functions, such as module-level monitoring, coordinated rapid shutdown, and enhanced diagnostics.
[0056] Depending on the selected embodiment, when the string inverter energizes the DC bus, each MLPE apparatus in the string powers up. It may operate either independently, executing its own instance of the claimed method without inter-device communication, or in a coordinated manner, exchanging data and control signals with adjacent one or more MLPE units or a central control device. This coordination supports features like system performance optimization, rapid shutdown, and enhanced monitoring at the module level. The processor within each apparatus executes its instance of the claimed method as described, either autonomously or as part of a coordinated system.
[0057] Each apparatus exclusively monitors the voltage (VPV) and current (IPV) of its own individually connected solar panel (110) via its dedicated sensors (102,103). It has no information regarding the operational state, voltage, current, or shading condition of any other panel in the string. For example, consider a string of ten panels, each with its own MLPE apparatus. If panel number three becomes partially shaded by a tree branch, only the processor on apparatus number three will detect the resulting abrupt voltage drop. It will then independently execute the claimed method: it will identify the hard shading condition, assess the power available in the different operating bins, and select the optimal strategy for its own panel, which may involve operating at a lower voltage to maximize its individual power contribution.
[0058] Simultaneously, the other nine unshaded panels and their respective MLPE devices will identify their condition as "unshaded" and will independently execute the strategy of operating at their nominal MPPT voltage. The DC-DC converter (101) within each apparatus (100) ensures that the output of each module contributes effectively to the overall string current and voltage. The string inverter, located downstream, sees only the aggregate V-I characteristic of the entire string and performs its own MPPT on this combined output. The rapid convergence of each individual MLPE device, is critical to ensuring that their independent operations do not interfere with the slower, system-level MPPT cycle of the string inverter. This embodiment demonstrates a distributed intelligence system where each module is self-optimizing, providing full support for the claimed apparatus (100) as a standalone device.
[0059] The self-learning mechanism is described as follows. The memory (106) includes a non-volatile portion configured as a database. Each time the algorithm converges on a stable, optimal operating point, the processor (105) stores a data tuple in this database. This tuple may include, for example: an identifier for the shading condition or operating bin, the determined optimal voltage, the optimal current, and the optimal duty cycle. Over time, the database is populated with a rich set of historical performance data under various real-world conditions. When a new MPPT operation begins, after identifying the current shading condition, the processor (105) queries this database to find the historical entry that most closely matches the current conditions. It then uses the stored optimal duty cycle from that historical entry as the starting point for its search. This technical feature allows the invention to leverage past experience, bypass a lengthy initial search phase, and converge on the MPP much more rapidly.
[0060] The technical superiority and practical utility of the present invention are demonstrated through a series of direct, empirical tests. The results, provide a clear validation of the claimed methods and apparatus, particularly when contrasted with the known deficiencies of conventional approaches.
[0061] Figure 4 serves as a critical baseline, illustrating the precise technical problem solved by the invention. This figure shows four time-series plots for a solar panel operating under the control of a conventional Constant Voltage MPPT algorithm. The plots are: Panel 3 Voltage (top-left), Panel 3 Duty cycle (top-right), Panel 3 Power (bottom-left), and Panel 3 Current (bottom-right). The experiment begins with the panel in an unshaded state, operating at its nominal voltage of approximately 38V. In the time region highlighted by the red dashed circles, a hard shadow is introduced. The immediate physical consequence is shown in the Voltage plot, where the panel voltage plummets from ~38V to approximately 15V due to bypass diode activation. The conventional algorithm, lacking the intelligent identification means of the present invention, responds by drastically altering the duty cycle, as shown in the Duty plot. As shown in the Current plot (yellow), this action causes the panel's output current to collapse. The end result, shown in the Power plot (red), is a catastrophic failure where the total power generated drops to less than 25W. This figure provides unequivocal evidence that simplistic MPPT strategies are not only inefficient but can be actively detrimental to power generation under hard shading.
[0062] Figure 5A and Figure 5B provide a direct comparative validation of the present invention's advanced strategic approach. These figures show the performance of a panel without the intelligent optimizer (Figure 5A, serving as a control) versus a panel equipped with the apparatus of the present invention (Figure 5B) under the same hard shadow condition.
[0063] In Figure 5A (Control Panel), when the hard shadow occurs, the panel's voltage drops to ~25V, and the power output stabilizes at a suboptimal level of approximately 70W.
[0064] In Figure 5B (Apparatus of the Present Invention), when the same hard shadow occurs, the voltage also drops to ~25V. However, the processor (105) of the present invention, executing the intelligent method, identifies the hard shading event and assesses the power available in the different operating bins. It correctly determines that operating at this lower voltage is the most power-efficient strategy. Consequently, instead of attempting to force the voltage higher, it maintains operation in this lower-voltage bin, resulting in a stable and significantly higher power output. This result provides clear, empirical evidence of the invention's ability to make a correct strategic decision to maximize power generation.
[0065] Referring again to Figure 1, the apparatus (100) of the invention is described. The MPPT Controller (104) comprises the processor (105) and memory (106).
[0066] The processor (105) may include one or more general purpose processors (e.g., INTEL® or Advanced Micro Devices® (AMD) microprocessors) and/or one or more special purpose processors (e.g., digital signal processors or Xilinx® System On Chip (SOC) Field Programmable Gate Array (FPGA) processor), MIPS/ARMclass processor, a microprocessor, a digital signal processor, an application specific integrated circuit, a microcontroller, a state machine, or any type of programmable logic array.
[0067] The memory (106) may include but is not limited to, non-transitory machine-readable storage devices such as hard drives, magnetic tape, floppy diskettes, optical disks, Compact Disc Read-Only Memories (CD-ROMs), and magnetooptical disks, semiconductor memories, such as ROMs, Random Access Memories (RAMs), Programmable Read-Only Memories (PROMs), Erasable PROMs (EPROMs), Electrically Erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions.
[0068] Any combination of the above features and functionalities may be used in accordance with one or more embodiments. In the foregoing specification, embodiments have been described with reference to numerous specific details that may vary from implementation to implementation. The specifications and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set as claimed in claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction.
[0069] The present invention, as fully described herein and defined by the appended claims, delivers a holistic and technically advanced solution that provides a plurality of significant and synergistic benefits over the prior art. The primary benefit is a substantial increase in the total energy harvested over the lifetime of the PV installation. This is achieved through the invention's ability to intelligently distinguish between different shading conditions and select the genuinely optimal operating bin, ensuring the solar panel (110) operates at its true global maximum power point where conventional methods would fail. This maximization of yield is further enhanced by the system's superior speed and stability, derived from the rapid search algorithm and the inventive reversion mechanism, which prevents energy-wasting oscillations. Furthermore, the self-learning capability provides a significant leap over static algorithms by leveraging historical data to reduce convergence time and continuously improve adaptive performance. Beyond immediate power gains, the invention offers a crucial long-term advantage by extending the operational lifetime and reliability of the solar panel (110); the intelligent strategy of operating at a lower voltage during hard shading, actively mitigates the formation of damaging hotspots, a primary cause of permanent panel degradation. Finally, these technical advantages are embodied in a practical, cost-effective, and widely compatible solution. As a standalone module-level power electronics (MLPE) device that operates independently, the invention can be easily integrated into new or existing solar installations using standard string inverters, providing a clear path for widespread adoption and a superior return on investment.
[0070] In the above description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present systems and methods. It will be apparent the systems and methods may be practiced without these specific details. Reference in the specification to “an example” or similar language means that a particular feature, structure, or characteristic described in connection with that example is included as described, but may not be included in other examples.
[0071] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily configure and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein. , C , Claims:I / We Claim:
1. A method for optimizing maximum power point tracking (MPPT) of a solar panel (110), the method comprising:
monitoring a voltage of the solar panel (110) to detect a voltage shift;
identifying a shading condition of the solar panel (110) based on characteristics of the detected voltage shift;
selecting, based on the identified shading condition, one of a plurality of power optimization strategies, each strategy corresponding to a different operational state of the solar panel (110);
executing the selected power optimization strategy to determine an optimal operating point for the solar panel (110); and
storing operational data associated with the optimal operating point in a memory (106) to facilitate future MPPT operations.
2. The method as claimed in claim 1, wherein identifying the shading condition comprises distinguishing between a gradual voltage shift within a first predetermined range and an abrupt voltage shift within a second, larger predetermined range.
3. The method as claimed in claim 2, wherein the first predetermined range is approximately 5-10% of the panel voltage and is associated with temperature changes or mild/diffuse shading, and wherein the second predetermined range is approximately 15% or more of the panel voltage and is associated with a bypass diode activation condition.
4. The method as claimed in claim 1, wherein the plurality of power optimization strategies correspond to distinct MPPT operating bins, each bin being associated with a specific bypass diode activation state.
5. The method as claimed in claim 4, wherein selecting one of the plurality of power optimization strategies comprises assessing a total achievable power output for each of the plurality of operating bins and selecting the bin yielding the highest total output.
6. The method as claimed in claim 3, wherein for the hard shading condition, the selected optimization strategy comprises operating the solar panel (110) at a lower voltage and a higher current corresponding to an activated bypass diode of a shaded portion of the solar panel (110) when said operation yields a higher total power output than operating at a nominal MPPT voltage.
7. The method as claimed in claim 3, wherein for the mild/diffuse shading or a temperature-driven voltage shift condition, the selected optimization strategy comprises operating the solar panel (110) at a nominal MPPT voltage to maximize total energy generation.
8. The method as claimed in claim 1, wherein executing the selected power optimization strategy comprises performing a rapid search within a selected voltage range to converge on the optimal operating point.
9. The method as claimed in claim 8, wherein performing the rapid search further comprises:
iteratively setting a new duty cycle based on a midpoint of a current search range;
monitoring a resulting panel voltage in response to the new duty cycle; and
reverting to a previous duty cycle if the resulting panel voltage shifts below a previous voltage by more than a predetermined voltage shift threshold.
10. The method as claimed in claim 8, wherein the search converges to an optimal operating point within a predetermined error margin of approximately 5% in five or fewer iterations.
11. The method as claimed in claim 1, wherein storing the operational data comprises storing observed voltage, current, and a duty cycle value, and wherein the stored operational data is utilized to determine an optimized MPPT starting point for a subsequent operation.

12. The method as claimed in claim 1, further comprising:
detecting a deactivation of at least one bypass diode; and
in response, resetting a duty cycle to restore the solar panel to a nominal MPPT voltage range.
13. The method as claimed in claim 1, wherein the method is performed by a module-level power electronics (MLPE) device that operates either without communication or with communication with one or more other MLPE devices.
14. An apparatus (100) for optimizing maximum power point tracking (MPPT) of a solar panel (110), the apparatus comprising:
a processor (105); and
a memory (106) operatively coupled to the processor (105), the memory (106) storing instructions that, when executed by the processor (105), cause the apparatus (100) to:
monitor a voltage of the solar panel (110) to detect a voltage shift;
identify a shading condition of the solar panel (110) based on characteristics of the detected voltage shift;
select, based on the identified shading condition, one of a plurality of power optimization strategies;
execute the selected power optimization strategy to determine an optimal operating point for the solar panel (110); and
store operational data associated with the optimal operating point in the memory (106) to facilitate future MPPT operations.
15. The apparatus (100) as claimed in claim 14, wherein the instructions to identify the shading condition further cause the apparatus (100) to distinguish between a gradual voltage shift within a first predetermined range of approximately 5-10% of the panel voltage associated with temperature changes or mild/diffuse shading, and an abrupt voltage shift within a second, larger predetermined range of approximately 15% or more of the panel voltage associated with a bypass diode activation condition.
16. The apparatus (100) as claimed in claim 15, wherein the instructions further cause the apparatus (100) to select the power optimization strategy by:
for the hard shading condition, selecting the strategy that comprises operating the solar panel (110) at a lower voltage corresponding to an activated bypass diode; and
for the mild/diffuse shading condition, selecting the strategy that comprises operating the solar panel (110) at a nominal MPPT voltage.
17. The apparatus (100) as claimed in claim 14, wherein the instructions to execute the selected power optimization strategy further cause the apparatus (100) to perform a rapid search comprising:
iteratively setting a new duty cycle;
monitoring a resulting panel voltage; and
reverting to a previous duty cycle if the resulting panel voltage drops by more than a predetermined threshold.
18. The apparatus (100) as claimed in claim 17, wherein the instructions further cause the search to converge to an optimal operating point within a predetermined error margin of approximately 5% in five or fewer iterations.