基于条件互信息特征选择法和Adaboost算法的电能质量复合扰动分类
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摘要
为准备识别复杂电能质量扰动类型,提出一种基于条件互信息平均最优化(avg-CMIM)特征选择法与Adaboost动态集成分类器的电能质量复合扰动分类策略。首先基于条件互信息,提出准确衡量特征与扰动类别相关性、特征集内部目标导向冗余性的评价准则,得到不同扰动标签相匹配的最优分类特征集。再利用Adaboost分类器进行动态增强学习,对未知样本进行标签识别,通过组合标签结果确定复合扰动的组成成分,实现电能质量复合扰动的识别。仿真结果表明,在不同程度噪音下,该方法能够高效准确地识别电压暂升、电压暂降、电压短时中断、谐波、脉冲暂态和振荡暂态等单一扰动和其组合成的复合扰动,并通过实测数据验证了方法的正确性和可行性。
To identify complex power quality disturbances, we proposed a new method for the classification of multiple power quality disturbances based on conditional mutual information maximization(avg-CMIM) feature selection method and Adaboost classifier. Based on the conditional mutual information, the correlation between the feature set and the disturbance category and the target-oriented redundancy among the feature set were evaluated, and the optimal classification feature sets corresponding to different disturbance labels were obtained. Then Adaboost was used to train and classify different disturbance labels, and the results of different disturbance labels were combined to identify the composition of multiple power quality disturbances. The simulation data show the proposed method in this paper can improve the classification quality of the multiple power quality disturbances including voltage swell, voltage sag, voltage interruption, harmonics, impulsive transient, oscillation transient, and their compound ones under different noisy environments. The correctness and feasibility of the method are verified by the measured data.
To identify complex power quality disturbances, we proposed a new method for the classification of multiple power quality disturbances based on conditional mutual information maximization(avg-CMIM) feature selection method and Adaboost classifier. Based on the conditional mutual information, the correlation between the feature set and the disturbance category and the target-oriented redundancy among the feature set were evaluated, and the optimal classification feature sets corresponding to different disturbance labels were obtained. Then Adaboost was used to train and classify different disturbance labels, and the results of different disturbance labels were combined to identify the composition of multiple power quality disturbances. The simulation data show the proposed method in this paper can improve the classification quality of the multiple power quality disturbances including voltage swell, voltage sag, voltage interruption, harmonics, impulsive transient, oscillation transient, and their compound ones under different noisy environments. The correctness and feasibility of the method are verified by the measured data.
引文
[1]黄建明,瞿合祚,李晓明.基于短时傅里叶变换及其谱峭度的电能质量混合扰动分类[J].电网技术,2016,40(10):3184-3191.HUANG Jianming,QU Hezuo,LI Xiaoming.Classification for hybrid power quality disturbance based on STFT and its spectral kurtosis[J].Power System Technology,2016,40(10):3184-3191.
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[15]黄南天,彭华,蔡国伟,等.电能质量复合扰动特征选择与最优决策树构建[J].中国电机工程学报,2017,37(3):776-785.HUANG Nantian,PENG Hua,CAI Guowei,et al.Feature selection and optimal decision tree construction of complex power quality disturbances[J].Proceedings of the CSEE,2017,37(3):776-785.
[16]ERI?TI H,?ZAL Y,ERI?TI B,et al.Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines[J].International Journal of Electrical Power&Energy Systems,2013,49:95-103.
[17]HAJIAN M,AKBARI F A,ABDOOS A A.New automated power quality recognition system for online/offline monitoring[J].Neurocomputing,2014,128(5):389-406.
[18]周雒维,管春,卢伟国.多标签分类法在电能质量复合扰动分类中的应用[J].中国电机工程学报,2011,31(4):45-50.ZHOU Luowei,GUAN Chun,LU Weiguo.Application of multi-label classification method to catagorization of multiple power quality disturbances[J].Proceedings of the CSEE,2011,31(4):45-50.
[19]李扬,顾雪平.基于改进最大相关最小冗余判据的暂态稳定评估特征选择[J].中国电机工程学报,2013,33(34):179-186.LI Yang,GU Xueping.Feature selection for transient stability assessment based on improved maximal relevance and minimal redundancy criterion[J].Proceedings of the CSEE,2013,33(34):179-186.
[20]张军,胡震波,朱新山.基于AdaBoost分类器的实时交通事故预测[J].计算机应用,2017,37(1):284-288.ZHANG Jun,HU Zhenbo,ZHU Xinshan.Real-time traffic accident prediction based on AdaBoost classifier[J].Journal of Computer Applications,2017,37(1):284-288.
[21]张杨,刘志刚.一种基于时频域多特征量的电能质量混合扰动分类新方法[J].中国电机工程学报,2012,32(34):83-90.ZHANG Yang,LIU Zhigang.A new method for power quality mixed disturbance classification based on time-frequency domain multiple features[J].Proceedings of the CSEE,2012,32(34):83-90.
[2]KHOKHAR S,ZIN A A B M,MOKHTAR A S B,et al.A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances[J].Renewable&Sustainable Energy Review,2015,51(11):1650-1663.
[3]BORGES F A S,FERNANDES R A S,SILVA I N,et al.Feature extraction and power quality disturbances classification using smart meters signals[J].IEEE Transactions on Industrial Informatics,2016,12(2):824-833.
[4]尹柏强,何怡刚,朱彦卿.一种广义S变换及模糊SOM网络的电能质量多扰动检测和识别方法[J].中国电机工程学报,2015,35(4):866-872.YIN Baiqiang,HE Yigang,ZHU Yanqing.Detection and classification of power quality multi-disturbances based on generalized S-transform and fuzzy SOM neural network[J].Proceedings of the CSEE,2015,35(4):866-872.
[5]徐志超,杨玲君,李晓明.基于聚类改进S变换与直接支持向量机的电能质量扰动识别[J].电力自动化设备,2015,35(7):50-58.XU Zhichao,YANG Lingjun,LI Xiaoming.Identification of power quality disturbance based on clustering improved S-transform and direct support vector machine[J].Electric Power Automation Equipment,2015,35(7):50-58.
[6]黄南天,张卫辉,蔡国伟,等.采用改进多分辨率快速S变换的电能质量扰动识别[J].电网技术,2015,39(5):1412-1418.HUANG Nantian,ZHANG Weihui,CAI Guowei,et al.Power quality disturbances classification with improved multiresolution fast S-transform[J].Power System Technology,2015,39(5):1412-1418.
[7]徐方维,杨洪耕,叶茂清,等.基于改进S变换的电能质量扰动分类[J].中国电机工程学报,2012,32(4):77-84.XU Fangwei,YANG Honggeng,YE Maoqing,et al.Classification for power quality short duration disturbances based on generalized S-transform[J].Proceedings of the CSEE,2012,32(4):77-84.
[8]OZGONENEL O,YALCIN T,GUNEY I,et al.A new classification for power quality events in distribution systems[J].Electric Power Systems Research,2013,95:192-199.
[9]莫文雄,许中,肖斐,等.基于随机矩阵理论的电力扰动事件时空关联[J].高电压技术,2017,43(7):2386-2393.MO Wenxiong,XU Zhong,XIAO Fei,et al.Research on temporal-spatial correlation of power disturbance events based on random matrix theory[J].High Voltage Engineering,2017,43(7):2386-2393.
[10]BENNASAR M,HICKS Y,SETCHI R.Feature selection using joint mutual information maximisation[J].Expert Systems with Applications,2015,42(22):8520-8532.
[11]管春.电能质量扰动分类中特征选择问题的研究[J].重庆邮电大学学报,2013,25(4):514-517.GUAN Chun.Feature selection in power quality event classification[J].Journal of Chongqing University of Posts and Telecommunications,2013,25(4):514-517.
[12]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,2017,95:246-259.
[13]RAY P K,MOHANTY S R,KISHOR N,et al.Optimal feature and decision tree-based classification of power quality disturbances in distributed generation systems[J].IEEE Transactions on Sustainable Energy,2014,5(1):200-208.
[14]姚旭,王晓丹,张玉玺,等.特征选择方法综述[J].控制与决策,2012,27(2):161-166.YAO Xu,WANG Xiaodan,ZHANG Yuxi,et al.Summary of feature selection algorithms[J].Control and Decision,2012,27(2):161-166.
[15]黄南天,彭华,蔡国伟,等.电能质量复合扰动特征选择与最优决策树构建[J].中国电机工程学报,2017,37(3):776-785.HUANG Nantian,PENG Hua,CAI Guowei,et al.Feature selection and optimal decision tree construction of complex power quality disturbances[J].Proceedings of the CSEE,2017,37(3):776-785.
[16]ERI?TI H,?ZAL Y,ERI?TI B,et al.Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines[J].International Journal of Electrical Power&Energy Systems,2013,49:95-103.
[17]HAJIAN M,AKBARI F A,ABDOOS A A.New automated power quality recognition system for online/offline monitoring[J].Neurocomputing,2014,128(5):389-406.
[18]周雒维,管春,卢伟国.多标签分类法在电能质量复合扰动分类中的应用[J].中国电机工程学报,2011,31(4):45-50.ZHOU Luowei,GUAN Chun,LU Weiguo.Application of multi-label classification method to catagorization of multiple power quality disturbances[J].Proceedings of the CSEE,2011,31(4):45-50.
[19]李扬,顾雪平.基于改进最大相关最小冗余判据的暂态稳定评估特征选择[J].中国电机工程学报,2013,33(34):179-186.LI Yang,GU Xueping.Feature selection for transient stability assessment based on improved maximal relevance and minimal redundancy criterion[J].Proceedings of the CSEE,2013,33(34):179-186.
[20]张军,胡震波,朱新山.基于AdaBoost分类器的实时交通事故预测[J].计算机应用,2017,37(1):284-288.ZHANG Jun,HU Zhenbo,ZHU Xinshan.Real-time traffic accident prediction based on AdaBoost classifier[J].Journal of Computer Applications,2017,37(1):284-288.
[21]张杨,刘志刚.一种基于时频域多特征量的电能质量混合扰动分类新方法[J].中国电机工程学报,2012,32(34):83-90.ZHANG Yang,LIU Zhigang.A new method for power quality mixed disturbance classification based on time-frequency domain multiple features[J].Proceedings of the CSEE,2012,32(34):83-90.