Multiple Open-Circuit Fault Diagnosis Based on Multistate Data Processing and Subsection Fluctuation Analysis for Photovoltaic Inverter

In this paper, a practical fault diagnosis algorithm is presented to realize multiple open-circuit fault diagnosis for photovoltaic (PV) inverters. By the feature analysis for the output currents of normal and fault states, a fault diagnosis algorithm is developed, which is composed of multistate data processing (MSDP) block, subsection fluctuation analysis (SSFA) block, and the artificial neural network (ANN) block. First, the MSDP block is used to distinguish the different feature data and to adopt the data processing scheme, which improves the smoothness of the main data, retains the main fault features, and removes the influence of load change. Second, the SSFA block is used to extract the data feature, which can be used to accurately distinguish the different states of any switch. Finally, ANN is used by combining with the proposed MSDP and SSFA to implement intelligent classification, in which the dependency and the number of thresholds can be reduced. Comparing with the existing fault classification algorithms, the proposed algorithm is simple and stable to realize the multiple switches fault diagnosis for PV inverters. It does not require additional hardware equipment, and it can reduce the complexity of design and realization. Finally, the effectiveness of the fault diagnosis algorithm is verified by the experimental results.