四川轻化工大学学报（自然科学版）2024，Vol.37Issue(2)：39-48,10.DOI:10.11863/j.suse.2024.02.06
基于ICEEMDAN与BiLSTM的交直流混联电网故障识别
Identification of Fault Types in AC-DC Hybrid Grids Based on ICEEMDAN and BiLSTM
摘要
Abstract
To address the problems of difficult fault feature extraction and low accuracy of fault type identification in hybrid AC-DC power grids,a fault type identification method of AC-DC hybrid grids has been proposed,which is based on improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)and honey badger algorithm,using to optimize the hyper parameters of bidirectional long and short term memory neural network.Firstly,ICEEMDAN is used to decompose the line fault voltage and current.Secondly,based on the joint feature index of variance contribution ratio and correlation coefficient,the modal components are selected and their peak-to-peak values are obtained,and the peak-to-peak values of the selected modal components are inputted as eigenvectors into the optimised bi-directional long and short term memory network of the honey badger algorithm for fault type identification.Finally,a hybrid AC-DC grid model is built in PSCAD/EMTDC to validate the proposed method.The experimental results show that the proposed algorithm can achieve 99.5%fault identification accuracy,which is less affected by different fault lines,fault transition resistance,fault location and data loss,and the fault identification accuracy for noise-containing signals can achieve 95.9%under 10 dB noise interference,which has strong adaptability.关键词
交直流混联电网/故障类型辨识/经验模态分解/蜜獾算法/双向长短时记忆神经网络Key words
AC-DC hybrid grids/fault type identification/empirical modal decomposition/honey badger algorithm/bidirectional long and short term memory neural network分类
信息技术与安全科学引用本文复制引用
陈伟哲,宋弘,吴浩,田海鹏,宋匡玮..基于ICEEMDAN与BiLSTM的交直流混联电网故障识别[J].四川轻化工大学学报（自然科学版）,2024,37(2):39-48,10.基金项目
四川省科技厅项目(2021YFG0313 （）
2022YFS0518 （）
2022ZHCG0035) （）
人工智能四川省重点实验室基金项目(2020RZY03) （2020RZY03）
四川轻化工大学人才引进项目(2021RC12) （2021RC12）
四川轻化工大学研究生创新基金项目(Y2022122) （Y2022122）
