Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion

Abstract

Fault diagnosis plays a crucial role in the operation and maintenance (O&M) of photovoltaic (PV) arrays, and reasonable feature extraction is a prerequisite for effective fault diagnosis. In this paper, a feature extraction and fault diagnosis method based on current–voltage (I–V) conversion is proposed. First, the PV array modeling method based on the double diode model (DDM) and the reverse bias model (RBM) is proposed. This modeling method can simulate the I–V curves under different states and provide data foundation for feature extraction and fault diagnosis. Next, three procedures for correcting I–V curves and three feature enhancement methods are compared to select the optimal program for I–V conversion. The converted feature matrix is dimensionalized using T-distributed stochastic neighbor embedding (T-SNE) to achieve feature extraction. Finally, ten classification models for fault diagnosis are adopted to verify the effectiveness of the proposed feature extraction method. Experimental results demonstrate that the proposed methods perform well on simulation data and provide satisfactory fault diagnosis results for the measured I–V curves. Among the classification models tested, the variable prediction model (VPM) shows the optimal comprehensive performance, with the computational time of 0.17 s and the accuracy of 99.4%.