Microgrid equivalent modeling based on long short-term memory neural network

Models of electrical equipment components are the basis of the transient stability studies of power system with multi-microgrids. Microgrid is a small local power system contains different electrical components which connected into distribution network through the Point of Common Couple(PCC). In order to simplify the grid-connect model of microgrid in power system stability study, a data-driven equivalent modeling method for microgrid based on Long Short-Term Memory(LSTM) recurrent neural network is proposed in this paper. LSTM recurrent neural network is the state-of-the-art models for a variety machine learning problems, and the dynamic behaviors of microgrid under grid-connect mode are presented by the LSTM recurrent neural network. The training data set should contain the dynamic behaviors of all components, the current and power of PCC are collected as the training data set during the faults for the parameter estimation of LSTM. Based on the measurement data set, a LSTM recurrent neural network structure with four inputs and two outputs is designed. During the training process, the real and imaginary parts of the current at present and previous time are taken as the input of the network, and errors of the output active and reactive power between equivalent model and detailed model of microgrid is taken as the evaluation index of the equivalent model method. An AC microgrid contains different distributed generations and load is built in DIgSILENT, and the accuracy of the proposed method is verified by comparing the equivalent model with the original detailed system.