Hybrid fuzzy C-means and deep learning framework for intelligent fault classification in solar PV systems

Abstract

Photovoltaic (PV) systems have proven themselves to be a viable alternative energy source; however, there are multiple faults related to PV systems which cause energy losses and low efficiencies. Manual or rule-based algorithms are traditionally used for fault diagnosis, which are not efficient and unsuitable for real-time applications. In this paper, a novel hybrid intelligent classification system for PV fault detection is proposed by integrating Fuzzy C-Means (FCM) clustering and Deep Learning (DL) techniques such as Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU). The dataset consists of 102,400 samples collected from a real-time 10 kW solar PV system operating under varying irradiance conditions ranging from 600 W/m2 to 1,000 W/m2 and temperature conditions ranging from 25 °C to 40 °C. The FCM technique is used to enhance the extracted features by clustering the membership functions, and the obtained features are used for model training. The performance of proposed models is evaluated using the classification metrics and confusion matrices. The proposed FCM + GRU model achieved 91.13% accuracy 0.79 precision, 0.76 recall, and F1-score of 0.78. The obtained results confirm the effectiveness of the proposed hybrid framework by improving fault classification performance under various operating environments.