基于BAS-BP分类器模型的电压暂降源识别

为提高不同电压暂降扰动源的识别正确率,对电压暂降进行有效治理,提出一种利用天牛须搜索(BAS)算法和反向传播(BP)神经网络构建BAS-BP分类器模型的电压暂降源识别方法。文中应用改进S变换提取16个特征指标,组成电压暂降源识别指标体系,为消除冗余信息对分类结果的影响,利用组合赋权法筛选出9个指标作为分类器的输入量。通过BAS算法对BP神经网络的初始权值和阈值寻优,构建BAS-BP分类器模型,实现对配电网不同类型电压暂降源的识别。仿真结果表明,该分类器模型具有一定的抗噪能力与适用性,并且与常规分类器模型相比,具有更好的分类效果。

Identification of voltage sag source based on BAS-BP classifier model

In order to improve the recognition accuracy of different voltage sag disturbance sources and effectively control the voltage sag, a method of voltage sag source identification based on beetle antennae search (BAS)-back propagation (BP) classifier model constructed by longicorn BAS and BP neural network is proposed. In this paper, the improved S-transform is used to extract 16 characteristic indicators to form a voltage sag source identification indicator system. In order to eliminate the influence of redundant information on the classification results, 9 indicators are selected as the input of the classifier using the combination weighting method. By optimizing the initial weights and thresholds of BP neural network by BAS, the BAS-BP classifier model is constructed to identify different types of voltage sag sources in distribution network. The simulation results show that the classifier model has certain anti-noise ability and applicability, and has a better classification than the conventional classifier model dose.