首页|一种数据库查询的多标签电能质量混合扰动识别与分类新方法

一种数据库查询的多标签电能质量混合扰动识别与分类新方法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
该文针对电能质量混合扰动的复杂性及当前分类识别的准确率不够高等问题，提出一种数据库查询的多标签电能质量混合扰动分类与识别方法，该方法能够更加科学准确地识别混合扰动，可为电能质量治理、扰动事件责任追究等提供有力决策辅助。首先，该方法基于可调 Q 因子小波变换(tunable Q-factor wavelet transform，TQWT)和时变均方根(root mean square，RMS)的特征提取方法有效提取扰动信号基频时域特征量，较好地克服了当前基频幅值特征提取准确率不够高的难点问题;其次，提出频域特征曲线分割新方法，高效地提取扰动信号的高频特征曲线;然后，建立基频幅值特征数据库和高频特征曲线数据库;最后，利用快速动态时间规整(dynamic time warping，DTW)结合多标签的分类思想进行混合电能质量扰动的多标签分类。仿真实验结果表明，新方法具有如下优势:几乎不受限值范围内基频偏移的影响，抗噪性较强，对单一扰动及包含双重、三重、四重扰动在内的 27 种扰动具有较高的分类准确率。电网实测扰动数据的分析，进一步验证了该方法的扰动识别有效性。
A New Multi-label Database Query Method for Combined Power Quality Disturbances Classification and Recognition
This paper proposes a new multi-label database query method for combined power quality disturbances(PQDs)recognition and classification,aiming at the problems of the complexity of combined PQDs and the insufficient accuracy of current classification.This new method can be used to recognize combined PQDs more scientifically and accurately,which can provide powerful decision-making assistance for PQ management and disturbance event accountability.First,this method employs the proposed feature extraction method based on the tunable Q-factor wavelet transform(TQWT)and time-varying root mean square(RMS)to effectively extract the fundamental time domain features from the PQDs,which is an effective way to overcome the current difficulties of insufficient accuracy in extracting fundamental amplitude features.Next,the proposed frequency-domain characteristic curve segmentation method is used to extract the high-frequency characteristic curve of the PQDs effectively.Then,the fundamental frequency amplitude feature database and the high-frequency characteristic curve database are established.The multi-label database query with fast dynamic time warping(DTW)is used to classify the combined PQDs effectively.The simulation results show that the new method has the following advantages:It is hardly affected by the fundamental frequency deviations within the range specified in the GB/T standard,and it has not only good noise tolerance capability,but also high classification accuracy for 27 kinds of PQDs,including single,double,triple,and quadruple disturbances.Finally,its effectiveness is further verified by the actual disturbance data collected from the power grid.

multi-label classification of combined disturbancesQ-factor wavelet transformtime-varying root mean squaresegmentation of characteristic curvefast dynamic time warping

王燕、李雨婕、卞安吉、骆玉深、江浙、曹浩敏

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国家民委重点实验室(西南民族大学电气工程学院),四川省 成都市 610041

黑龙江科技大学电气与控制工程学院,黑龙江省 哈尔滨市 150022

混合扰动多标签分类 可调Q因子小波变换 时变均方根 特征曲线分割 快速动态时间规整

四川省科技创新苗子工程项目西南民族大学中央高校基本科研业务费专项资金项目

2022027ZYN2022091

2024

10.13334/j.0258-8013.pcsee.230475

中国电机工程学报
中国电机工程学会

中国电机工程学报

CSTPCD北大核心
影响因子：2.712
ISSN：0258-8013
年,卷(期)：2024.44(15)