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文章摘要
改进BiLSTM在电力变压器故障诊断中的应用研究
Research on the application of improved BiLSTM in power transformer fault diagnosis
Received:May 15, 2023  Revised:June 07, 2023
DOI:10.19753/j.issn1001-1390.2024.05.022
中文关键词: 电力变压器  故障诊断  双向长短期记忆网络  鲸鱼优化算法  混合策略
英文关键词: power transformer, fault diagnosis, bidirectional long short-term memory, whale optimization algorithm, hybrid strategy
基金项目:河北省自然科学基金资助项目(F2021502013)
Author NameAffiliationE-mail
zhangshouyan* School of Electrical Engineering,SEU,Jiangsu Nanjing zhangshouyan73@163.com 
shiweigang Hebei Xibaipo Electric Power Co, Ltd hebei,shijiazhuan zhangshouyan73@163.com 
yangliguo Hebei Xibaipo Electric Power Co, Ltd hebei,shijiazhuan zhangshouyan73@163.com 
peiyuehui Hebei Xibaipo Electric Power Co, Ltd hebei,shijiazhuan zhangshouyan73@163.com 
yaochao Hebei Xibaipo Electric Power Co, Ltd hebei,shijiazhuan zhangshouyan73@163.com 
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中文摘要:
      针对目前电力变压器故障诊断方法存在的诊断准确率低、特征参数标准不一等问题,在分析电力变压器故障的基础上,提出了一种双向短期记忆网络与改进鲸鱼优化算法相结合的电力变压器故障诊断方法。引入混合策略(权重和收敛因子优化、蝙蝠算法和莱维飞行策略)对鲸鱼优化算法进行优化,并利用优化后的鲸鱼优化算法寻找双向短期记忆网络的最优参数建立电力变压器故障诊断模型。通过算例与常规方法进行对比分析,验证了该方法的优越性。结果表明,相比于常规方法,所提故障诊断方法具有更高的故障诊断准确率和最佳的实际应用效果,故障诊断准确率分别提高了10.42%和7.85%,为电力变压器故障诊断提供了一种新的思路。
英文摘要:
      In response to the problems of low diagnostic accuracy and inconsistent characteristic parameter standards in current power transformer fault diagnosis methods, based on the analysis of power transformer faults, a fault diagnosis method of power transformer based on bidirectional short-term memory (BiLSTM) network and improved whale optimization algorithm is proposed. The hybrid strategy (weight and convergence factor optimization, bat algorithm and Levy flight strategy) is introduced to optimize the whale optimization algorithm, and the optimized whale optimization algorithm is used to find the optimal parameters of the bidirectional short-term memory network to establish the power transformer fault diagnosis model. The superiority of this method is verified through comparative analysis between numerical examples and conventional methods. The results show that compared to conventional methods, the proposed fault diagnosis method has higher fault diagnosis accuracy and the best practical application effect, the fault diagnosis accuracy has been improved by 10.42% and 7.85%, providing a new approach for power transformer fault diagnosis.
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