首页|基于ICEEMDAN与BiLSTM的交直流混联电网故障识别

基于ICEEMDAN与BiLSTM的交直流混联电网故障识别

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
针对交直流混联电网故障特征提取困难与故障类型识别准确率低的问题,提出了一种基于改进的完全自适应噪声集合经验模态分解(ICEEMDAN)和蜜獾算法优化双向长短时记忆神经网络(BiLSTM)超参数的交直流混联电网故障类型辨识方法.首先,利用ICEEMDAN对线路故障电压电流进行分解;然后,基于方差贡献率与相关系数联合特征指标选择模态分量并求其峰峰值,以选出的模态分量的峰峰值作为特征向量输入至蜜獾算法优化后的双向长短时记忆网络进行故障类型辨识.最后,在PSCAD/EMTDC中搭建了交直流混联电网模型对所提方法进行验证.实验结果表明,所提算法故障辨识准确率达到99.5%,受不同故障线路、故障过渡电阻、故障位置、数据丢失影响较小,并且对10 dB噪声干扰下的含噪声信号的故障辨识准确率达到95.9%,具有较强的适应性.
Identification of Fault Types in AC-DC Hybrid Grids Based on ICEEMDAN and BiLSTM
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.

AC-DC hybrid gridsfault type identificationempirical modal decompositionhoney badger algorithmbidirectional long and short term memory neural network

陈伟哲、宋弘、吴浩、田海鹏、宋匡玮

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四川轻化工大学自动化与信息工程学院,四川 宜宾 644000

人工智能四川省重点实验室,四川 宜宾 644000

阿坝师范学院,四川 阿坝 623002

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

四川省科技厅项目四川省科技厅项目四川省科技厅项目人工智能四川省重点实验室项目四川轻化工大学人才引进项目四川轻化工大学研究生创新基金

2021YFG03132022YFS05182022ZHCG00352020RZY032021RC12Y2022122

2024

10.11863/j.suse.2024.02.06

四川轻化工大学学报(自然科学版)
四川理工学院

四川轻化工大学学报(自然科学版)

影响因子：0.44
ISSN：2096-7543
年,卷(期)：2024.37(2)
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