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.
［1］Khokhar S, Zin A A M, Memon A P, et al. A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network［J］. Measurement, 2017, 95(1): 246-259.
［2］周铁生, 刘洋, 牛益国, 等. 基于S变换的配电网零序导纳故障选线方法［J］. 燕山大学学报, 2018, 42(5): 394-399.
Zhou T S, Liu Y, Niu Y G, et al. Zero sequence admittance fault line selection method for distribution network based on S transform ［J］. Journal of Yanshan University, 2018, 42(5): 394-399.
［3］Babu N R, Mohan B J. Fault classification in power systems using EMD and SVM［J］. Ain Shams Engineering Journal, 2017, 8(2): 103-111.
［4］曹玲芝, 刘俊飞, 郑晓婉. 基于EEMD的HHT在电能质量多扰动分类识别中的应用［J］. 电气技术, 2017, 18(4): 66-70.
Cao L Z, Liu J F, Zheng X W. Classification and Recognition of Power Quality Multi-disturbance based on EEMD-HHT［J］. Electrical Engineering, 2017, 18(4): 66-70.
［5］Kantelhardt, J W, Zschiegner, S A, et al. Multifractal detrended fluctuation analysis of nonstationarytime series［J］. Physica A: Statistical Mechanics and its Applications, 2002, 316(14): 87-114.
［6］孙斌, 姚海涛. 风电场风速时间序列的多重分形去趋势波动分析［J］. 电工技术学报, 2014, 29(6): 204-210.
Sun B, Yao H T. Multi-fractal Detrended Fluctuation Analysis of Wind Speed Time Series in Wind Farm［J］. Transactions of China Electrotechnical Society, 2014, 29(6): 204-210.
［7］Zhang N, Hu G, Zhang X, et al. Wrist pulse recognition based on multi-fractal spectrum［C］//2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2016.
［8］张宇辉, 王劼妍, 武东斌, 等. 基于多重分形降趋波动分析法的谐振接地系统故障选线新判据［J］. 电测与仪表, 2017, 54(20): 23-28.
Zhang Y H, Wang J Y, Wu D B, et al. A new criterion of fault line selection in resonant grounding system based on multifractal detrended flucuation analysis［J］. Electrical Measurement & Instrumentation, 2017, 54(20): 23-28.
［9］ Zhang X, Zhao J M, Li H P, et al. Gearbox Fault Diagnosis Based on Multifractal Detrended Fluctuation Analysis and Improved K Means Clustering［C］//Prognostics & System Health Management Conference, 2018.
［10］Chatterjee S, Pratiher S, Bose R. Multifractal Detrended Fluctuation Analysis Based Novel Feature Extraction Technique for Automated Detection of Focal and Non Focal EEG Signals［J］. Iet Science Measurement & Technology, 2017, 11(8): 1014-1021.
［11］Zhu H J, Zhang W G. Multifractal property of Chinese stock market in the CSI 800 index based on MF-DFA approach［J］. Physica A Statistical Mechanics & Its Applications, 2018, 490(1): 497-503.
［12］时培明, 梁凯, 赵娜, 等. 基于分形维数和GA-SVM的风电机组齿轮箱轴承故障诊断［J］. 计量学报, 2018, 39(1): 61-65.
Shi P M, Liang K, Zhao N, et al. Fault Diagnosis of Wind Turbine Gearbox Bearing Based on Fractal Dimension and GA-SVM［J］. Acta Metrologica Sinica, 2018, 39(1): 61-65.
［13］Ma Y J, Zhai M Y. Fractal and multifractal features of the broadband power line communication signals［J］. Computers & Electrical Engineering, 2018, 72: 566-576.
［14］Sikdar D, Roy R, Mahadevappa M. Epilepsy and seizure characterisation by multifractal analysis of EEG subbands［J］. Biomedical Signal Processing & Control, 2018, 41: 264-270.
［15］杨胡萍, 李辉, 占思凯, 等. 基于理想点多属性决策算法的中长期电力负荷组合预测［J］. 燕山大学学报, 2017, 41(1): 63-67.
Yang H P, Li H, Zhan S K, et al. Medium and long-term power load combination forecasting based on ideal-point and multi-attribute decision algorithm［J］. Journal of Yanshan University, 2017, 41(1): 63-67.
［16］戴银娟, 付石磊. 基于改进C4. 5算法的新型车辆故障预测方法研究［J］. 常熟理工学院学报, 2019, 33(5): 72-77.
Dai Y J, Fu S L. Research on New Vehicle Fault Prediction Method Based on Improved C4. 5 Algorithm ［J］. Journal of Changshu Institute of Technology(Natural Sciences), 2019, 33(5): 72-77.
［17］张立国, 张淑清, 李莎莎, 等. 电能质量扰动识别的不同时频分析方法研究［J］. 计量学报, 2017, 38(3): 345-350.
Zhang L G, Zhang S Q, Li S S, et al. Study on the Detection and Classification of Power Quality Disturbances Using Difference Time-frequency Methods［J］. Acta Metrologica Sinica, 2017, 38(3): 345-350.
［18］林海雪. 电能质量指标的完善化及其展望［J］. 中国电机工程学报, 2014, 34(29): 5073-5079.
Lin H X. Perfecting Power Quality Indices and Prospect［J］. Proceedings of the CSEE, 2017, 38(3): 345-350.
2021, Vol. 42 
