RNN-based Fault Detection Method for MMC Photovoltaic Gridconnected System

Abstract

Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through Modular Multilevel Converters (MMC) during the development. Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds, and the long detection time. Method: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN). Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker. Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.