Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Abstract

Due to the importance of determining faulty bypass diodes in photovoltaic systems, faulty bypass diodes have been of widespread interest in recent years due to their importance to improving PV system durability, operation, and overall safety. This paper presents new work in developing an artificial intelligence (AI) based model using the principles of artificial neural networks (ANN) to detect short and open PV bypass diodes fault conditions. With only three inputs from the PV system, namely the output power, short-circuit current, and open-circuit voltage, the developed ANN model can determine whether the PV bypass diodes are defective. In the experimentally validated case of short and open bypass diodes, 93.6% and 93.3% of faulty bypass diodes can be detected. Furthermore, the developed ANN model has an average precision and sensitivity of 96.4% and 92.6%, respectively.

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

“…A technique based on fast Fourier transform and ANN was able to detect open-circuit and short-circuit faults in the 5-level cascaded inverter using the inverter's output voltage [87,88]. Dhimish et al showed that ANNs can detect partial shading, short circuits, ground faults, and degradation faults, with an efficiency of approximately 99% for correctly classified defects [89]. An ANN-based approach is proposed to detect and classify series, parallel, short-circuit, and open-circuit resistance faults under different irradiation and temperature conditions in a PV system [90].…”

“…The results of the simulation and experimental study show a good correlation with a classification error rate of 2.7%. Dhimish et al also found that ANNs are useful in the detection of bypass diode faults in short circuits and open circuits, with the model being 96.4% and 92.6% accurate in detecting short-circuit and open-circuit bypass diode faults, respectively [89]. In a review of the application of ANNs in the diagnosis of PV systems, Li et al emphasized the importance of ANNs in the field of solar photovoltaics [21].…”

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

“…[16][17][18] Liu et al 19 introduce a novel clustering method based on dilation and erosion theory, with enhanced fault diagnosis in PV arrays without the need for predetermining fault types. Research efforts have often been fragmented, focusing on specific fault types through methodologies, like, fuzzy logic, 20 neural networks, [21][22][23][24][25] and machine learning 26 leaving a void for a comprehensive fault detection and localization solution. 27 While macroscale fault detection within PV arrays has achieved success, microscale fault localization at the module level remains challenging, often requiring costly wireless sensor networks for detailed module insights, thereby increasing deployment expenses.…”