| 引用本文: | 阮梓航,肖先勇,胡文曦,郑子萱,汪 颖.基于多粒度特征选择和模型融合的复合电能质量扰动分类特征优化[J].电力系统保护与控制,2022,50(14):1-11.[点击复制] |
| RUAN Zihang,XIAO Xianyong,HU Wenxi,ZHENG Zixuan,WANG Ying.Multiple power quality disturbance classification feature optimization based onmulti-granularity feature selection and model fusion[J].Power System Protection and Control,2022,50(14):1-11[点击复制] |
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| 摘要: |
| 现代电力系统因其“双高”特性造成电能质量扰动模式愈加复杂，对复合扰动的准确分类提出了挑战。传统电能质量扰动分类方法在特征提取阶段所提取的特征由人为确定，难以判断所提取的特征对分类问题是否有效，加之多重复合扰动特征相互耦合导致扰动特征的可分性确定困难。为此，提出一种基于粒度的计算方法进行特征选择的模型。在提取的扰动特征集的基础上，通过构建多粒度空间反映特征分布差异性，进而挖掘各粒度下的最优特征子集以确定有效和冗余的分类特征，达到优化分类效果的目的。在此基础上，通过集成分类模型融合不同粒度空间最优扰动特征集所训练的同质弱分类器模型，提出一种新的电能质量扰动多粒度集成分类方法。该方法克服了现有方法在进行多粒度分类时通过寻找最优单粒度空间特征而导致的其他粒度空间信息丢失的问题。实验表明，多粒度特征选择算法可提取对分类有效的扰动特征，集成分类模型可进一步改善模型的分类性能。 |
| 关键词: 电能质量 复合扰动 特征选择 多粒度空间 集成分类 |
| DOI：DOI: 10.19783/j.cnki.pspc.211199 |
| 投稿时间：2021-08-31修订日期：2021-11-11 |
| 基金项目:国家自然科学基金项目资助(51807126)；中央高校基本科研业务费专项资金资助 |
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| Multiple power quality disturbance classification feature optimization based onmulti-granularity feature selection and model fusion |
| RUAN Zihang,XIAO Xianyong,HU Wenxi,ZHENG Zixuan,WANG Ying |
| (College of Electrical Engineering, Sichuan University, Chengdu 610065, China) |
| Abstract: |
| Modern power systems with “double high” characteristics make power quality disturbance patterns more complex, and the accurate classification of multiple power quality disturbances becomes more difficult. In the feature extraction stage of traditional power quality disturbance classification methods, the extracted features are determined artificially. Thus, it is difficult to judge whether the extracted features are adequate for classification problems, and the coupling of multiple feature distribution will affect the separability of disturbance features. Therefore, this paper proposes a feature selection method based on granular computing to optimize the performance of the classification. Based on the original feature set, a multi-granularity space is constructed to reflect the difference in feature distribution. Then the optimal feature subsets under each granularity are mined to determine the effective and redundant classification features. The homogeneous base classifiers trained by optimal feature subsets corresponding to different granularity spaces are fused by the ensemble model. A new multi-granularity ensemble classification model for power quality disturbance is proposed. This method overcomes the problem of the existing techniques by searching for the optimal valuable information of a single granularity space in a multi-granularity classification, leading to other granularity spaces losing the useful information. The simulation results show that the multi-granularity feature selection algorithm can extract useful features for classification, and an integrated model can improve the classification performance of the model.
This work is supported by the National Natural Science Foundation of China (No. 51807126). |
| Key words: power quality multiple disturbance feature selection multi-granularity space integrated classification |
