摘要
针对用于矿井中的油浸式变压器故障诊断问题,提出了一种基于布谷鸟搜索(CS)算法优化BP神经网络的新算法,利用CS算法优化网络的阈值和权值并将优化后的参数用于BP神经网络进行故障诊断,以提高收敛速度和诊断正确率。通过对变压器故障实例分析,表明该算法具有实用性和有效性。
In order to solve the fault diagnostic problems of oil-immer transformer used in the mine, a new fault diagnosis method based on improved BP neural network is proposed. The cuckoo search algorithm is used to optimize the weights and threshold parameters of BP neural network to improve the speed of convergence and fault diagnosis accuracy. Subsequently, it is concluded that the optimized algorithm is practical and effective through an example analysis.
作者
陈尔奎
韩清春
周栾
CHEN Er-kui, HAN Qing-chun, ZHOU Luan(College of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, Chin)
出处
《煤炭技术》
CAS
2018年第6期223-224,共2页
Coal Technology
