Research on Fault Prediction and Health Management of Grid-Connected Inverter based on Data Drive

Abstract

Grid-connected inverter is the core component of photovoltaic power generation system, and its stable operation is very important to the overall performance of the system. However, the inverter is easily influenced by environmental factors and its own complexity, which leads to frequent failures and affects power generation efficiency and power grid stability. Firstly, an innovative fault prediction model of grid-connected inverter is constructed by using machine learning and deep learning technology. The prediction accuracy and robustness of the model are improved by integrating the prediction results of basic learners such as decision tree and support vector machine (SVM). The experimental results show that the integrated learning model achieves an accuracy of 0.95 and a F1 score of 0.94 under the optimal parameter configuration. At the same time, the Long Short Term Memory Network (LSTM) model is introduced to effectively capture the complex nonlinear relationship and time dependence in the data, which further improves the prediction performance, and the highest accuracy rate can reach 0.96. Based on the fault prediction model, a comprehensive health management strategy of grid-connected inverter is formulated in this paper. The strategy framework includes four links: data collection and monitoring, fault prediction and evaluation, preventive maintenance plan formulation, fault early warning and response. By monitoring the key parameters of the inverter in real time and comparing with the fault prediction model, the early warning and timely response of the fault can be realized. The experimental results show that after the implementation of health management strategy, the failure rate of inverter is significantly reduced, the response time of fault warning is significantly shortened, and the operation reliability is significantly improved.