Photovoltaic fault detection algorithm using ensemble learning enhanced with deep neural network feature engineering - Maryam Parvin; Hossein Yousefi; Behnam Mohammadi-Ivatloo (Results in Engineering)
Reliable fault detection is critical for the widespread adoption of photovoltaic (PV) systems, contributing significantly to global efforts to mitigate climate change. Traditional machine learning (ML) approaches often require large input training datasets and face challenges in scenarios where sufficient data is not available. This paper introduces an innovative methodology that integrates advanced deep neural network (DNN)-based feature extraction with ensemble learning (EL) to achieve precise fault detection in PV systems under limited training data availability. The utilized EL techniques include bagging, random forest, AdaBoost, Gradient Boost, and X-Gradient Boost. Fault diagnosis and detection (FDD) were performed in two separate stages. In the first stage, the presence of a faulty condition was diagnosed by comparing the actual power output with the predicted power. For this purpose, a power prediction model based on the SANDIA empirical power forecast model was employed. A Hybrid metaheuristic grey wolf/particle swarm optimization algorithm was implemented to extract the optimum values of the empirical constants of the power forecast model. The fault classification was conducted in the second stage using the optimum EL algorithm enhanced with Deep Neural Network (DNN) feature extraction. The capability of the DNN feature extraction, presented for the first time in this study, in identifying more complex and meaningful data features for small-sized input training datasets significantly enhanced the overall classification accuracy. The developed novel algorithm demonstrated transformative potential, particularly when integrated with EL strategies. Experimental validation of the proposed PV FDD algorithm was conducted for a 6.24 kW PV array. The random forest EL methodology with DNN feature extraction yielded 99.2 % fault classification accuracy, which was the highest among the studied EL techniques. The incorporation of the developed technique led to notable fault detection accuracy enhancement across EL methods, with increase of 4.9 %, 5.3 %, 4.9 %, and 3.4 % in accuracy for detecting line-to-line, open-circuit, short-circuit, and partial shading faults, respectively. Furthermore, the F1-scores for the detection of the mentioned faults were found to be 99 %, 99 %, 100 %, and 99 %, respectively.