摘要
针对变压器故障诊断中BP神经网络诊断精度不够高的缺陷,提出一种天牛须搜索(BAS)算法与BP神经网络相结合的算法.将天牛须搜索算法寻优后的初始权值和阈值作为BP神经网络的初始权值和阈值,建立模型并进行仿真测试.结果表明,与传统BP神经网络相比,新模型有效克服了训练时间长、收敛速度慢的缺点,为变压器故障诊断提出了一种新的方法.
Aiming at the problems that BP neural network diagnosis accuracy is not high enough defect in transformer fault diagnosis,an algorithm combining the BP neural network with the beetle antennae search algorithm is proposed.The initial weights and thresholds of the optimization algorithm are taken as the initial weights and thresholds of the BP neural network,the model is presented and the simulation test is performed.The results showed that,compared with the traditional BP neural network,the new model overcomes the shortcoming of long training time and slow convergence.The presented method for transformer fault diagnosis is new.
作者
陈华
王强
刘晓坤
CHEN Hua;WANG Qiang;LIU Xiaokun(Electrical Engineering Institute,Xinjiang University,Urumqi 830047,China)
出处
《信阳师范学院学报（自然科学版）》
CAS
北大核心
2020年第4期635-639,共5页
Journal of Xinyang Normal University(Natural Science Edition)
基金
国家自然科学基金项目(50767003)。
