Cyclostationary analysis for fault detection in PV inverters

Abstract

Ensuring the reliability of photovoltaic (PV) inverters is crucial for the stable operation of PV systems. Traditional fault detection methods based on time-domain or frequency-domain analysis often struggle with noise and disturbances, limiting their sensitivity and effectiveness. This paper presents a novel fault detection approach utilizing cyclostationary analysis to enhance the identification of transistor faults in PV inverters. By exploiting the cyclostationary properties of the inverter voltage signal, we decompose it into periodic and residual components to extract fault signatures. The cyclic autocorrelation function (CAF) is computed for the residual signal, revealing hidden periodicities linked to fault conditions. The proposed method is validated by modeling PV panels under various conditions using the Bishop model and analyzing the impact of transistor open-circuit and short-circuit faults on inverter performance. Comparative analysis reveals that CAF exhibits superior fault sensitivity compared to conventional root mean square (RMS) metrics, making it a promising tool for early and robust fault detection. This approach contributes to improving PV system reliability and maintenance efficiency, paving the way for advanced diagnostic techniques in power electronics.