基于混沌集成决策树的电能质量复合扰动识别

Recognition of hybrid PQ disturbances based on a chaos ensemble decision tree

针对电能质量复合扰动类别多、特征关联性强及识别错误率较高的问题,提出了一种基于混沌集成决策树的电能质量复合扰动识别方法。首先参考IEEE标准,给出了常见的7种单一电能质量扰动和16种电能质量复合扰动的信号模型,并批量生成扰动波形样本。然后针对上述扰动的特性差异,通过S变换时频域分析,设计和提取出9种扰动时频域特征。最后利用集成学习集体能力和混沌搜索优势,构建混沌集成决策树,并有效完成了电能质量复合扰动识别。仿真实验和142组实测数据验证结果表明,该方法对于23种扰动的分类准确率高于基本决策树、复杂决策树及加权最近邻法等方法,具有良好的应用前景。

Given the problems of multiple types,strong feature correlation and the high recognition error rate of hybrid Power Quality(PQ)disturbances,a hybrid PQ disturbance recognition method based on a chaos ensemble decision tree is proposed.First,from the IEEE standard,the common signal models of 7 kinds of single PQ disturbances and 16 kinds of hybrid PQ disturbances are obtained,and disturbance waveform samples are generated in batches.Then through S-transform time-frequency domain analysis,9 features of disturbance in the time-frequency domain are designed and extracted according to the difference of these disturbances.Finally,taking advantage of the collective ability of ensemble learning and chaotic search,a chaotic ensemble decision tree is constructed,and the identification of hybrid PQ disturbances is effectively completed.Simulation experiments and a 142 field data test show that for 23 types of disturbances,the recognition accuracy of the proposed method is higher than that of basic decision trees,complex decision trees and weighted nearest neighbor method,and has good application prospects.