高压电器2025，Vol.61Issue(2)：83-92,101,11.DOI:10.13296/j.1001-1609.hva.2025.02.010
基于反馈约束变分模态分解和LSTM网络的并联电抗器油温预测研究
Study on Oil Temperature Prediction of Shunt Reactor Based on Feedback Constrained Variational Mode Decomposition and Long Short-term Memory Neural Network
摘要
Abstract
Shunt reactor is an important power equipment in the power grid.Accurate estimation of its oil tempera-ture variation trend can provide an important basis for early fault monitoring and warning.An kind of oil temperature prediction model of shunt reactor based on feedback-constrained variational mode decomposition(VMD)and long short-term memory neural network(LSTM)is proposed.Firstly,the signal feedback constrains and weighted sample entropy are used to optimize VMD decomposition number k and penalty factors α to form a VMDFS decomposition method,decompose the original oil temperature sequence of the shunt reactor into multiple sets of smooth sub-se-quence so to eliminate the influence of the unsteady information.Then,the LSTM neural network prediction models for each sub-sequence is set up and particle swarm is used to optimize the number of neurons.Finally,the predicted oil temperature of each sub-sequence is superimposed to obtain the final estimated oil temperature of the shunt reac-tor.It is proved through the analysis of the measurement data of a 220 kV shunt reactor at a substation that the pro-posed model has such advantages as high accuracy,strong robustness and slow accumulation of errors in single-step and multi-step estimation performance,and can effectively reflect to the oil temperature variation trend of shunt reac-tor,as well as provide reference for its monitoring and early warning.关键词
并联电抗器/油温预测/VMDFS/LSTM神经网络Key words
shunt reactor/oil temperature prediction/VMDFS/long short-term memory neural network(LSTM)引用本文复制引用
丁文涛,淡淑恒,陈浩宇,蔡立川..基于反馈约束变分模态分解和LSTM网络的并联电抗器油温预测研究[J].高压电器,2025,61(2):83-92,101,11.基金项目
国家自然科学基金项目(50577040).Project Supported by National Natural Science Foundation of China(50577040). （50577040）
