电力系统保护与控制2024，Vol.52Issue(24)：74-84,11.DOI:10.19783/j.cnki.pspc.240256
基于MHA-CNN-SLSTM和误差补偿的短期互感器误差预测
Short-term transformer error prediction based on MHA-CNN-SLSTM and error compensation
摘要
Abstract
To improve the accuracy of instrument transformer error prediction,first,an antagonistic search operator strategy and nonlinear convergence control factor are introduced to improve the traditional seagull algorithm.A method based on the improved Seagull optimization algorithm(ISOA)to optimize the key parameters of variational mode decomposition(VMD)is proposed to realize the adaptive decomposition of error data.Then,based on the multi-head attention(MHA)mechanism,the cross-processing of error-influencing features is used to mine the correlation between each feature,and the deep relationship between the weakly correlated features and errors is established through the relationship between the strongly correlated features and the errors,so as to avoid the reduction of prediction accuracy caused by data waste.Considering the relationship between the training set and the test set,a long short-term memory(SLSTM)neural network considering the similarity of samples is proposed to dynamically adjust the network weights and biases.Based on this,the MHA-CNN-SLSTM prediction model is constructed,and the error between the predicted value and the actual value is re-input into the prediction model as the training set,and the compensation data is generated to compensate for the preliminary predicted value and further improve the predicted value.Finally,the measured data of a transformer is used to verify the results,and the results show that the proposed model has higher prediction accuracy and effect.关键词
超短期预测/变分模态分解/多头注意力机制/LSTM/误差补偿Key words
ultra-short-term forecasting/VMD/multi-head attention mechanism/LSTM/error compensation引用本文复制引用
陈豪钰,李振华,张绍哲,程江洲,李振兴,邱立..基于MHA-CNN-SLSTM和误差补偿的短期互感器误差预测[J].电力系统保护与控制,2024,52(24):74-84,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.52277012). 国家自然科学基金项目资助(52277012) （No.52277012）
武汉强磁场学科交叉基金项目资助(WHMFC202202) （WHMFC202202）
