Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph

Fault diagnosis of photovoltaic array plays an important role in operation and maintenance of PV power plant. The nonlinear characteristics of photovoltaic array and the Maximum Power Point Tracking technology in the inverter prevent conventional protection devices to trip under certain faults which reduces the system's efficiency and increases the risks of fire hazards. In order to better diagnose photovoltaic array faults under Maximum Power Point Tracking conditions, the sequential data of transient in time domain under faults are analyzed and then applied as the input fault features in this work. Firstly, the sequential current and voltage of the photovoltaic array are transformed into a 2-Dimension electrical time series graph to visually represent the characteristics of sequential data. Secondly, a Convolutional Neural Network structure comprising nine convolutional layers, nine max-pooling layers, and a fully connected layer is proposed for the photovoltaic array fault diagnosis. The proposed model for photovoltaic array fault diagnosis integrates two main parts, namely the feature extraction and the classification. Thirdly, this model automatically extracts suitable features representation from raw electrical time series graph, which eliminates the need of using artificially established features of data and then employs for photovoltaic fault diagnosis. Moreover, the proposed Convolutional Neural Network based photovoltaic array fault diagnosis method only takes the array of voltage and current of the photovoltaic array as the input features and the reference panels used for normalization. The proposed approach of photovoltaic array fault diagnosis achieved over 99% average accuracy when applied to the case studies. The comparisons of the experimental results demonstrate that the proposed method is both effective and reliable.