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| 多重分形去趋势波动分析及改进决策树在电能质量分析中的应用 | |
| 引用本文： | 张淑清,张赟,刘海涛,胡皓,李华,姚玉永,刘勇,王涛. 多重分形去趋势波动分析及改进决策树在电能质量分析中的应用[J]. 计量学报, 2021, 42(4): 424-431. DOI: 10.3969/j.issn.1000-1158.2021.04.04 |
| 作者姓名： | 张淑清 张赟 刘海涛 胡皓 李华 姚玉永 刘勇 王涛 |
| 作者单位： | 燕山大学电气工程学院,河北秦皇岛066004;东北大学信息科学与工程学院,辽宁沈阳110004;国网冀北电力有限公司唐山供电公司,河北唐山063000 |
| 基金项目： | 中央引导地方科技发展专项资金项目;河北省自然科学基金重点项目;河北省重点研发计划项目;2019年国网冀北唐山供电公司电网参数性能评估及无线监测;国家重点研发项目 |
| 摘 要： | 通过多重分形去趋势波动分析方法分析了6种常见的电能质量信号,证明了电能质量信号具有多重分形特征.据此提出基于多重分形去趋势波动分析的电能质量特征提取方法,选取多重分形谱参数(hqmax、αmin、α0)和信号能量E作为特征向量矩阵,结合改进决策树分类,进行电能质量分析和识别.该方法与DTCWT、HHT和EEMD方法进行... |
| 关 键 词： | 计量学 多重分形去趋势波动分析 特征提取 改进决策树 电能质量分析 |
| 收稿时间： | 2019-11-15 |
Application in power Quality Analysis Based on Multifractal Detrended Fluctuation Analysis and Improved Decision Tree |
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| ZHANG Shu-qing,ZHANG Yun,LIU Hai-tao,HU Hao,LI Hua,YAO Yu-yong,LIU Yong,WANG Tao. Application in power Quality Analysis Based on Multifractal Detrended Fluctuation Analysis and Improved Decision Tree[J]. Acta Metrologica Sinica, 2021, 42(4): 424-431. DOI: 10.3969/j.issn.1000-1158.2021.04.04 | |
| Authors: | ZHANG Shu-qing ZHANG Yun LIU Hai-tao HU Hao LI Hua YAO Yu-yong LIU Yong WANG Tao |
| Affiliation: | 1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China 2. Institute of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China 3. Tangshan Power Supply Company of North Hebei Electric Power Co. Ltd, Tangshan, Hebei 063000, China |
| Abstract: | Six common power quality signals are analyzed by multi-fractal and trend fluctuation analysis, which proves that the power quality signal has multiple fractal features. Based on this, a power quality feature extraction method based on multi-fractal detrended wave analysis is proposed. Multi-fractal spectrum parameters (hqmax、αmin、α0) and signal energy E are selected as feature vector matrix, combined with improved decision tree classification for power quality. Analysis and identification. The method is compared with DTCWT, HHT and EEMD. The results show that the proposed method shows better recognition results and provides a new idea for feature extraction of power quality signals. |
| Keywords: | metrology multifractal detrended fluctuation analysis feature extraction improved decision tree power quality analysis |
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