SS20: An Improved BPNN-Based Fault Diagnosis Method with Multi-Domain Feature Extraction for PV Array

To enhance diagnostic efficiency and precision, this paper proposes a novel fault diagnosis method that combines multi-domain feature extraction with a Back Propagation Neural Network (BPNN). First, the characteristic curves of photovoltaic (PV) array current-voltage (I-V) and power-voltage (P-V) are analyzed to identify key feature parameters such as voltage, current, and power. Traditional fault detection methods often rely on unimodal information from either the time domain <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(D_{t})$</tex> or frequency domain <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(D_{f})$</tex>, resulting in incomplete characterizations. To address this limitation, this study utilizes multi-domain feature extraction from the time domain, frequency domain, and time-frequency domain <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(D_{tf})$</tex> to construct a multi-domain feature set. Variational Mode Decomposition (VMD) is used to prevent over-decomposition during feature extraction in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D_{tf}$</tex>. Second, a fault diagnosis method is developed by optimizing the neural network weights and thresholds using the Circle Adaptive-Grey Wolf Optimizer (CA-GWO) algorithm. Experimental re-sults demonstrate that the proposed multi-domain BPNN-based approach effectively and accurately detects faults such as short-circuit <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(F_{sc})$</tex>, open-circuit <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(F_{oc})$</tex>, abnormal aging <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(F_{aa})$</tex>, and local shading <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(F_{ls})$</tex> in PV arrays. Furthermore, extensive testing shows that the proposed method significantly improves the accuracy and speed of fault diagnosis compared to classical BPNN methods.