基于互高阶谱MUSIC法的电机定子匝间短路故障特征分量提取  

Fault feature component extraction of generator stator inter-turn short-circuit based on MUSIC method for cross-high-order spectrum

作　　者：王洪希[1] 刘诤 田伟[1] 

机构地区：[1]北华大学电气信息工程学院,吉林吉林132021 [2]吉林化建电气工程有限公司,吉林吉林132011

出　　处：《电力系统保护与控制》2010年第23期117-120,132,共5页Power System Protection and Control

摘　　要：针对电机定子绕组匝间短路时,定子电流中干扰信号影响大,故障信号较微弱等缺点,研究了一种基于互高阶累积量的多重信号分类的故障特征检测方法(Multiple Signal Classification,MUSIC)。通过MUSIC算法对定子电流信号进行快速分解,形成噪声子空间和信号子空间,确定定子匝间短路故障特征频率分量。由于互高阶累积量可以有效地抑制相关和非相关噪声,在混合噪声条件下,该方法仍具有很高的谱分辨率和谱估计性能。仿真和实验结果表明,该方法在对电机定子匝间短路故障检测时,在不需要对分析数据进行整周期采样前提下,更准确地反映故障特征频率,证明了此方法的有效性。When generator stator windings inter-turn short circuit occurs, the disturbing signal in stator currents is strong and the fault signal is weak, thus multiple signal classification （MUSIC） method based on cross-high-order cumulate is proposed to overcome the shortcomings. MUSIC algorithm can decompose stator current signals quickly into noise subspace and signal subspace, and the frequency component of stator inter-turn short circuit feature is obtained. Since cross-high-order cumulate can restrict the correlated and uncorrelated noise, MUSIC method still shows high spectrum resolution and strong spectral estimation ability in the condition of mixed noise. The effectiveness of the proposed method is proved in simulation experiments that it reflects the fault feature frequency accurately without full period sampling of analysis data when the stator windings inter-turn short circuit fault is detected.

关 键 词：多信号分类 互高阶谱 匝间短路 故障检测 

分 类 号：TM307.1[电气工程—电机]