基于ISSA-XGBoost的电能质量扰动识别方法研究

A power quality disturbance identification method based on ISSA-XGBoost

针对传统电能质量扰动(power quality disturbances,PQDs)识别中特征提取有冗余,识别精度不高等问题,提出了一种基于改进麻雀搜索算法(improved sparrow search algorithm,ISSA)优化特征选择和极致梯度提升(eXtreme gradient boosting,XGBoost)的电能质量扰动识别方法。首先对电能质量扰动信号进行S变换,提取61种电能质量特征。再通过ISSA同时选择最优特征子集和XGBoost中最优参数,剔除冗余特征,提高识别精度。最后根据优化后的最优特征子集和XGBoost实现电能质量扰动的识别。仿真结果表明,所提出的方法能有效选择最优特征子集,对噪声环境下的19种电能质量扰动信号进行高效识别,并且具有较高的识别精度。

To solve the problem of redundancy and low recognition accuracy in traditional power quality disturbance recognition,this paper proposes a method based on an improved sparrow search algorithm(ISSA)to optimize feature selection and eXtreme gradient boosting(XGBoost).First,the power quality disturbance signal is transformed by S-transform,and 61 kinds of power quality characteristics are extracted.Then ISSA selects the optimal feature subset and the optimal parameters in XGBoost at the same time to eliminate redundant features and improve recognition accuracy.Finally,the power quality disturbance is identified according to the optimized feature subset and XGBoost.The simulation results show that the method proposed can effectively select the optimal feature subset,and efficiently identify 19 kinds of power quality disturbance signals in a noisy environment,and has high recognition accuracy.