基于GRU和GWO-KELM的电力线路故障诊断

Power line fault diagnosis based on GRU and GWO-KELM

为实现电力线路故障的高精度检测和分类,设计并实现了基于机器学习的电力线路故障诊断系统,核心模块是机器学习经典算法中门控循环单元(GRU)神经网络和核极限学习机(KELM)。利用GRU对电力数据进行故障诊断,将正常数据与故障数据高精度地区分开来;利用灰狼优化(GWO)算法对KELM的核参数和惩罚因子进行寻优,使KELM获得了最佳参数;利用KELM进行故障分类,成功将不同种类的故障区分开。试验证明,GRU在数据集的准确率高达98%,得到了最优参数的KELM在数据集中准确率高达99%;利用模拟退火算法(SA)进行了准确率比对,证实了GWO算法的优越性。还对数据集中的电压和电流进行了数据可视化,简洁直观地表达了数据集,为电力线路故障诊断提供了一个切实有效的方法。

To improve the detection precision and classification accuracy of power line faults,a power line fault diagnosis system based on machine learning is designed and implemented,whose core modules are Gated Recurrent Unit(GRU)neural network—A classical algorithm of machine learning,and Kernel Extreme Learning Machine(KELM).First,GRU is used to separate fault data from normal data accurately in electrical fault diagnosis.Then,the kernel parameters and penalty factors of KELM are optimized by Grey Wolf Optimization(GWO).The KELM with optimal parameters can successfully distinguish different types of faults.Proven by experimental data,GRU's accuracy in dataset classification is as high as 98%,and the KELM with the optimal parameters has an accuracy of 99%.Comparing the accuracies of the algorithms obtained by Simulated Annealing(SA),the superiority of GWO can be confirmed.The voltage and current data are visualized,presenting the dataset concisely and intuitively.This article provides a practical and effective method for diagnosing power line faults.