Capacitive voltage transformer measurement error prediction by improved long short-term memory neural network
Feng Zhou, Peng Zhao, Lei Min, Changxi Yue, Jicheng Yu, Siyuan Liang
Abstract
This paper proposes an improved Long Short-Term Memory neural network (LSTM) for Capacitor Voltage Transformer (CVT) measurement error prediction. The proposed model introduces bidirectional memory, deep feature extraction, and multi-task learning strategies to improve LSTM for high accuracy and high convergence speed. Then, the network parameters are optimized from the multi-source heterogeneous data of CVT, which combines the advantages of prior knowledge and intelligent prediction method. Finally, the trained model is utilized to predict the measurement error automatically. Ablation experiment results show that the three improved strategies can increase prediction accuracy. Comparative experiments show that the proposed improved LSTM model is more accurate than the mainstream machine learning algorithms, such as support vector, random forest, and entropy weight combination forecasting. The proposed improved LSTM model can achieve the CVT ratio error and angle error prediction more accurately and more rapidly than mainstream deep learning algorithms, such as convolutional neural networks, recurrent neural networks, and LSTM.