Research on power quality disturbance analysis and identification based on LSTM - Qian Wang; Xue Liang; Sichen Qin (Energy Reports, 2022)
In view of the cumbersome and inaccurate process caused by manual feature extraction in power quality disturbance classification, according to the characteristics of power quality classification and time sequence. This article presents a method of power quality disturbance analysis and identification based on LSTM. Firstly, the random single electric energy signal is spliced into a large signal to form a continuous electric energy signal sequence. Secondly, based on the existing neural network, an LSTM model suitable for PQD classification is constructed, and then the spliced large signals are used as input to train and optimize the model. The LSTM model will classify different power quality disturbances. Finally, six common power quality disturbances such as voltage sag, voltage swell, interruption, impact, oscillation and harmonic are simulated and verified respectively. The analysis results show that the high accuracy of the method is reflected, which proves the correctness and effectiveness of the proposed method, and is suitable for the power quality disturbance identification system.