电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2014年
4期
27-33
,共7页
电能质量%电压暂降%改进S变换%支持向量机%分类识别
電能質量%電壓暫降%改進S變換%支持嚮量機%分類識彆
전능질량%전압잠강%개진S변환%지지향량궤%분류식별
power quality%voltage sag%generalized S-transform%support vector machine%classification and identification
电压暂降是较常见、影响较大的电能质量问题,识别电压暂降扰动源对改善和治理电压暂降具有重要意义。分析了由线路短路故障、感应电动机启动、变压器投入等单一电压暂降扰动源和复合电压暂降扰动源引起的电压暂降现象,提出采用改进S变换分析复合电压暂降扰动源识别特征。根据基频幅值曲线和2~5倍基频幅值和曲线,从统计量、熵和能量等方面构建电压暂降识别特征指标,将这些特征指标作为支持向量机的输入实现对不同类型电压暂降扰动源的分类识别。仿真结果表明,采用改进S变换构建电压暂降识别特征指标比标准S变换在电压暂降扰动源分类识别上效果更好。
電壓暫降是較常見、影響較大的電能質量問題,識彆電壓暫降擾動源對改善和治理電壓暫降具有重要意義。分析瞭由線路短路故障、感應電動機啟動、變壓器投入等單一電壓暫降擾動源和複閤電壓暫降擾動源引起的電壓暫降現象,提齣採用改進S變換分析複閤電壓暫降擾動源識彆特徵。根據基頻幅值麯線和2~5倍基頻幅值和麯線,從統計量、熵和能量等方麵構建電壓暫降識彆特徵指標,將這些特徵指標作為支持嚮量機的輸入實現對不同類型電壓暫降擾動源的分類識彆。倣真結果錶明,採用改進S變換構建電壓暫降識彆特徵指標比標準S變換在電壓暫降擾動源分類識彆上效果更好。
전압잠강시교상견、영향교대적전능질량문제,식별전압잠강우동원대개선화치리전압잠강구유중요의의。분석료유선로단로고장、감응전동궤계동、변압기투입등단일전압잠강우동원화복합전압잠강우동원인기적전압잠강현상,제출채용개진S변환분석복합전압잠강우동원식별특정。근거기빈폭치곡선화2~5배기빈폭치화곡선,종통계량、적화능량등방면구건전압잠강식별특정지표,장저사특정지표작위지지향량궤적수입실현대불동류형전압잠강우동원적분류식별。방진결과표명,채용개진S변환구건전압잠강식별특정지표비표준S변환재전압잠강우동원분류식별상효과경호。
Among various types of power quality problems, voltage sag is more common and influential. The identification of voltage sag disturbance sources has great significance to improve the power quality. Different voltage sags, caused by single voltage sag disturbance sources such as short-circuit fault, starting induction motor, transformer energization and composite voltage sag disturbance sources, are analyzed. This paper proposes to analyze identification features of composite voltage sag disturbance sources based on generalized S-transform. According to the fundamental-frequency amplitude curve and sum of amplitude curve of 2nd to 5th harmonic, the feature indices of voltage sag are constructed in terms of statistics, wave morphology, entropy and energy. Then support vector machine (SVM) is employed to perform the identification of different types of voltage sag disturbance sources. The simulation results show that using feature indices of voltage sag based on generalized S-transform is better than those based on standard S-transform in identification of voltage sag disturbance sources.