高压电气节点温度的预测模型

Prediction algorithm for high temperature electrical node temperature

针对已有高压开关柜温度预警系统预测精度不高的问题,分析影响高压开关柜电气节点温升的因素,采用前向级联LM-BP神经网络提升算法运算速度,利用熵权法确定各因素权重,建立了熵权法与前向级联LM-BP神经网络相结合的预测模型。结果表明,所建模型的预测值更接近于真实值,与BP神经网络预测模型相比,预测精度提高约2倍。仿真实验验证了所建模型预测高压开关柜节点温度的有效性。

This paper aims to address the lower prediction accuracy affecting temperature warning system in existing high-voltage switch cabinets. The study involves analyzing the factors controlling the temperature rise of the high voltage switchgear electrical nodes; increasing the speed the forward cascaded LM-BP neural network and determining the weight of each factor weight method, and thereby developing a prediction model combining the entropy weight method with forward cascaded LM-BP neural network. The results show that the proposed model features the prediction value of the model closer to the true value and provides a prediction accuracy about 2 times higher than the BP neural network prediction model. Simulation experiments verify the effectiveness the model predicts the temperature of the high-voltage switchgear node.