Self-Diagnostic Fault Detection Systems for Solar Cells

Self-powered solar module fault detection system that enables real-time monitoring of solar panel bypass diodes through a thermoelectric device. The system comprises a thermoelectric element in contact with the bypass diode, a power storage unit for storing generated thermoelectric power, a communication unit transmitting power information, and a determination unit that determines module fault status based on the communication unit's power output. The system operates by generating power from the thermoelectric element when normal operating conditions are met, and immediately detecting and reporting failure when the communication unit fails to transmit power within a predetermined time threshold. This enables rapid fault detection even in the absence of external power sources.

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Detecting solar cell string faults in solar power systems by analyzing current measurements. The system measures current from each solar string in a connection box, compares the measured current to the maximum current, and calculates the percentage of current reduction. This percentage is compared against the maximum current to identify strings with reduced output. The system can also detect faults by comparing current levels across multiple strings, enabling early detection of system-wide issues.

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Failure diagnosis method, device, and system for solar panels that enables early detection of module failures through monitoring DC current ripple patterns. The method detects anomalies in DC current waveform characteristics, particularly an increase in ripple amplitude or frequency deviation, indicative of module failure. This approach bypasses traditional power monitoring methods by analyzing the DC current signal's inherent characteristics, eliminating the need for external power monitoring. The detection system continuously monitors DC current signals from the solar panel string, enabling early detection of module failures even when power output is not visibly affected.

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Low-cost, high-accuracy system for detecting abnormalities in solar cells like decreased power generation and hot spots. The system periodically acquires the operating voltage of a solar cell string. If the time-series variation in voltage exceeds a threshold, it indicates an abnormality in the cell string. This allows detecting cell issues without additional environmental sensors.

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A method for detecting potential degradation in solar cells through early detection of partial degradation (PD) in the solar cell's internal resistance network. The method employs a parallel resistance model of the solar cell, which is generated from measurements of the cell's AC impedance. By analyzing the model's response to partial degradation, the method identifies changes in the parallel resistance that indicate degradation. This approach enables earlier detection of PD compared to traditional methods that rely on full degradation thresholds.

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Fault detection system for photovoltaic modules and strings that uses machine learning to analyze the performance of photovoltaic arrays. The system measures array performance from both the array and module perspectives, calculates a performance metric based on environmental conditions, and identifies faults by comparing measured performance against estimated performance. The system employs an AI algorithm to analyze the characteristic curves of both array and module levels, enabling accurate fault detection even when the array is composed of multiple modules.

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Photovoltaic cell fault detection using a modulated light matrix approach. The method involves generating modulated light signals at different frequencies to each photovoltaic cell, then superimposing these signals to form a total short-circuit current. By analyzing the amplitude of the generated photocurrents, the method can determine the presence and location of faults in individual cells, including cracking, contact resistance issues, and internal resistance variations. This approach enables precise fault location and characterization through the unique spectral signatures of each cell.

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Detecting faults in photovoltaic systems using IV curves and advanced signal processing techniques. The method combines IV curve analysis with machine learning algorithms to identify and quantify faults in photovoltaic panels, strings, and fields. By leveraging IV curves and advanced signal processing, the method can accurately diagnose faults such as shading, dirt accumulation, short circuits, and bypass diode failures, enabling proactive maintenance and reducing system downtime.

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Photovoltaic power generation system with enhanced diagnostic capabilities through data analysis of voltage and current measurements. The system identifies aging issues by comparing average module voltage across strings, rather than relying solely on string-level data. It employs statistical analysis to detect deviations from normal operation patterns, enabling accurate diagnosis of module and string failures. The system also incorporates environmental factors like solar radiation and temperature into its diagnostic framework, providing comprehensive analysis of system performance.

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Photovoltaic module fault diagnosis method and device for determining current mismatch faults in solar panels. The method involves measuring the voltage-current characteristic of the solar panel using a diagnostic tool, which analyzes the panel's behavior across different operating conditions. The diagnostic tool detects deviations in the panel's voltage-current curve indicative of current mismatches, such as those caused by shading or temperature variations.

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Fault classification for photovoltaic arrays using graph signal processing. The method employs graph theory to develop a classification system that automatically identifies PV array faults beyond traditional diagnostic capabilities. It analyzes data from the PV array's power output, irradiance, and environmental conditions to classify faults into four distinct categories: standard test conditions, shading, degraded modules, and short circuits. The classification is achieved through a graph-based approach that leverages the properties of graphs to represent the PV array's behavior, enabling accurate fault detection and diagnosis.

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Decentralized solar power generation monitoring and control system for solar panels using a mesh network. The system enables autonomous monitoring and control of individual solar panels through wireless mesh communication, enabling self-diagnosis and bypassing of faults. The system detects DC arc faults between panels and between panels and the panel itself, with automatic switching and bypassing of failed panels. The monitoring and control are achieved through a distributed architecture where each panel has its own monitoring and control unit, enabling continuous operation even during failures.

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A photovoltaic module fault detection system for solar panels that enables real-time monitoring of module performance through integrated chip-level monitoring. The system comprises a photovoltaic cell module, an information transmission module integrated on the chip, a fault detection module, and an upper computer. The chip-based module contains monitoring circuits that continuously monitor the cell's electrical characteristics, while the information transmission module transmits data to the fault detection module for analysis. The fault detection module then identifies and reports any anomalies or potential issues through the upper computer, enabling proactive maintenance and early intervention.

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Fault diagnosis of solar power systems that prevents fires through enhanced monitoring of photovoltaic (PV) panel performance. The system analyzes PV array characteristics under normal and partial shading conditions to detect electrical faults. It tracks maximum power points and analyzes their behavior across varying insolation levels. The system sets specific detection criteria based on PV characteristic changes, enabling accurate fault detection even when partial shading occurs. This approach provides enhanced protection against electrical faults in solar power systems.

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Accurately diagnosing abnormalities in solar cell modules using voltage and current measurements as well as visual inspection. The method involves connecting a detection circuit to one module to measure voltage and current, determining if there's an output abnormality based on those values. For visual inspection, applying voltage to all modules to emit light, taking images, and analyzing them to find appearance abnormalities. This allows both electrical and physical diagnosis of solar cell modules.

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A solar cell voltage measurement system for individual fault diagnosis in photovoltaic systems. The system comprises a solar cell array with multiple solar cells connected in series or parallel, each with a bypass unit controlling electrical flow. A dedicated voltage sensor measures the array's output voltage. The system enables remote monitoring of individual solar cells through the sensor, with the bypass unit automatically switching between normal and bypass modes based on sensor output. A server manages the system, providing diagnostic capabilities through a network interface.

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Fault diagnosis for solar power systems through advanced data analysis and machine learning algorithms. The system employs a dedicated diagnostic communication unit that receives monitored data from the solar panel, analyzes it using a data analysis unit, and employs a failure database to detect anomalies. The system incorporates preset parameters for detecting specific faults, such as aging, damage, or obstruction, and stores both the monitored data and the diagnostic results. This enables proactive maintenance by identifying potential issues before they cause system performance degradation.

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Determining the presence of aging photovoltaic module faults in a solar string system without installing individual voltage or current sensors. The method analyzes power data from the connected modules to detect abnormal power variations, specifically identifying deviations from the normal power-voltage characteristic curve. The analysis is based on a secondary derivative of the power output, which captures the aging-induced power waveform characteristics. By comparing the power output against predetermined thresholds, the system determines whether a module has reached an abnormal state.

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A photovoltaic module fault detection system for solar power generation that enables real-time monitoring of individual panel performance. The system comprises a photovoltaic cell module, an information transmission module, a fault detection module, and an upper computer. The information transmission module includes a measurement module, a code module, and a component communication module. The measurement module connects to the output terminal of the photovoltaic cell module, the encoding module connects to the input terminal of the component communication module, and the component communication module connects to the input terminal of the upper computer. This configuration enables precise monitoring of individual solar panels through their own measurement capabilities, while the upper computer processes and analyzes the data to identify potential faults.

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Solar photovoltaic fault detection system and method that enables automatic, intelligent, and self-learning fault diagnosis through machine learning. The system combines numerical simulation modeling of the solar photovoltaic system with real-time data acquisition and measurement of environmental parameters. The simulation model is trained using a combination of initial data and actual system performance, and then corrected to improve accuracy. The system uses machine learning algorithms to establish multiple diagnostic models that can identify different types of faults based on the collected data. This enables rapid fault detection and diagnosis in solar photovoltaic systems, while also allowing for continuous model evolution and adaptation to changing system conditions.

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