基于BA-MKELM的微电网故障识别与定位

Microgrid Fault Identification and Location Based on BA-MKELM

提出一种基于贝叶斯算法优化多核极限学习机的微电网故障识别和定位方法。针对极限学习机输入参数和隐含层节点数随机选取导致回归能力不足的问题,引入核函数,将多项式与高斯径向基核函数加权组合构成多核极限学习机建立故障识别与定位模型,并采用贝叶斯算法对多核极限学习机相关参数进行优化,进一步提高模型的逼近能力。为了验证所提模型的故障识别与定位性能,选用极限学习机和多核极限学习机分别建立故障诊断模型进行比较分析。实验结果表明,所提方法能够高性能地识别和定位微电网中任何类型的故障,识别和定位精度更高。

A microgrid fault identification and location method based on Bayesian algorithm optimizing multi-kernel extreme learning machine is proposed.Aiming at the problem of insufficient regression ability caused by the random selection of input parameters and hidden layer nodes of extreme learning machine,the kernel function is introduced,and the polynomial and the Gaussian radial basis kernel function are combined to form a multi-kernel extreme learning machine to establish a fault identification and location model.The Bayesian algorithm is used to optimize the relevant parameters of the multi-kernel extreme learning machine to further improve the approximation ability of the model.In order to verify the fault identification and location performance of the proposed model,extreme learning machine and multi-kernel extreme learning machine are selected to establish fault diagnosis models respectively for comparative analysis.Experimental results show that the proposed method can identify and locate any type of faults in the microgrid with high performance,and has higher recognition and location accuracy.