电网技术
電網技術
전망기술
POWER SYSTEM TECHNOLOGY
2013年
5期
1272-1278
,共7页
电能质量%扰动识别%S变换%动态测度法%支持向量机%决策树
電能質量%擾動識彆%S變換%動態測度法%支持嚮量機%決策樹
전능질량%우동식별%S변환%동태측도법%지지향량궤%결책수
power quality%disturbance recognition%S-transform,dynamic measure method%support vector machine%decision tree
提出一种新型电能质量扰动识别方法，该方法采用快速傅里叶变换(fast Fourier transform，FFT)结合动态测度法提取3种特征以及S变换提取4种特征；采用决策树和支持向量机(support vector machine，SVM)设计组合分类器。针对 FFT 频谱中谐波频率明显的扰动类型，采用极值点包络的动态测度法提取频谱中的主要频率点特征，结合 S 变换提取的特征首先将扰动类型进行初步归类，然后采用 S变换的2个特征就能进行后续分类；决策树分类过程中采用SVM 来区分电压暂降和中断，克服了特征阈值随信噪比(signal-to-noise ratio，SNR)变化难以确定的问题。仿真实验表明，该方法能够准确识别包含2种复合扰动在内的11种电能质量扰动信号，SNR 低至 20 dB 时准确率仍达到96.50%；且与已有文献的分类结果对比表明，该方法准确率高，稳定性强，在低SNR条件下分类结果优势明显。
提齣一種新型電能質量擾動識彆方法，該方法採用快速傅裏葉變換(fast Fourier transform，FFT)結閤動態測度法提取3種特徵以及S變換提取4種特徵；採用決策樹和支持嚮量機(support vector machine，SVM)設計組閤分類器。針對 FFT 頻譜中諧波頻率明顯的擾動類型，採用極值點包絡的動態測度法提取頻譜中的主要頻率點特徵，結閤 S 變換提取的特徵首先將擾動類型進行初步歸類，然後採用 S變換的2箇特徵就能進行後續分類；決策樹分類過程中採用SVM 來區分電壓暫降和中斷，剋服瞭特徵閾值隨信譟比(signal-to-noise ratio，SNR)變化難以確定的問題。倣真實驗錶明，該方法能夠準確識彆包含2種複閤擾動在內的11種電能質量擾動信號，SNR 低至 20 dB 時準確率仍達到96.50%；且與已有文獻的分類結果對比錶明，該方法準確率高，穩定性彊，在低SNR條件下分類結果優勢明顯。
제출일충신형전능질량우동식별방법，해방법채용쾌속부리협변환(fast Fourier transform，FFT)결합동태측도법제취3충특정이급S변환제취4충특정；채용결책수화지지향량궤(support vector machine，SVM)설계조합분류기。침대 FFT 빈보중해파빈솔명현적우동류형，채용겁치점포락적동태측도법제취빈보중적주요빈솔점특정，결합 S 변환제취적특정수선장우동류형진행초보귀류，연후채용 S변환적2개특정취능진행후속분류；결책수분류과정중채용SVM 래구분전압잠강화중단，극복료특정역치수신조비(signal-to-noise ratio，SNR)변화난이학정적문제。방진실험표명，해방법능구준학식별포함2충복합우동재내적11충전능질량우동신호，SNR 저지 20 dB 시준학솔잉체도96.50%；차여이유문헌적분류결과대비표명，해방법준학솔고，은정성강，재저SNR조건하분류결과우세명현。
A new approach to recognize power quality disturbances is proposed. Based on fast Fourier transform (FFT) combined with dynamic measure method three kinds of features in power quality disturbance signals are extracted and using S-transform four features in power quality disturbance signals are extracted, and by use of decision tree and support vector machine (SVM) a combination classifier is designed. Firstly, for disturbance types with evident harmonic frequencies in FFT spectrum the features of main frequency points in FFT spectrum are extracted by the extreme point-enveloped dynamic measure method, and combining with the features extracted by S-transform, the disturbance types are preliminarily classified into several categories, and then by use of the two features extracted by S-transform the follow-up classification can be implemented. During the classification of decision tree the SVM is used to distinguish voltage sag from voltage interruption, thus the trouble that the feature thresholds, which vary with signal-to- noise ratio (SNR), are hard to be determined can be overcome. Simulation experiments show that using the proposed method eleven power quality disturbance signals, including two kinds of compound disturbances, can be accurately recognized, and when SNR is lowered to 20 dB the recognition accuracy can still reach to 96.50%. Comparison of the obtained results with reported classification results shows that the proposed method is accurate, stable and can be utilized in environment of low SNR.
