Nonlinear Load Harmonic Prediction Method Based on Power Distribution Internet of Things

Abstract

A large number of nonlinear loads have an impact on the stable operation of the power system. To solve this problem, this article proposes a nonlinear load harmonic prediction method based on the architecture of Power Distribution Internet of Things. Firstly, this method integrates the characteristics of edge computing technology and Power Distribution Internet of Things technology and proposes a Power Distribution Internet of Things framework applied to nonlinear load harmonic prediction, which provides top-level design for subsequent harmonic prediction methods of Power Distribution Internet of Things; then, considering the electrical characteristics of the typical nonlinear load, the mathematical model of nonlinear load data is constructed based on the harmonic coupling admittance matrix model on the edge side. At the same time, a nonlinear load harmonic prediction model based on dynamic time warping and long-term and short-term memory network (DTW-LSTM) is established in the cloud computing center to realize high accuracy and high real-time prediction and analysis of nonlinear load harmonics. Finally, the simulation results based on the general data set show that the MAE evaluation index of the proposed method is less than 5% in the experimental group, which shows good generalization ability, and has some advantages over the current method in operation efficiency.