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机构地区:[1]河海大学可再生能源发电技术教育部工程研究中心,南京211100
出 处:《微电机》2014年第9期42-45,共4页Micromotors
基 金:国家自然科学基金项目(51177039);教育部博士点基金项目(20090944110011)
摘 要:对双馈异步发电机转子绕组不对称故障进行监测可以有效降低电机故障发生的概率,减少风场运行维护费用。本文提出采用小波分解与小波能量谱相结合的方法提取双馈电机转子绕组不对称故障的特征量。并在实验室搭建了双馈电机故障实验平台,采用在转子A相串联电阻的方式模拟转子绕组不对称故障。通过对转子A相电流进行小波分解,找出故障频带,利用小波能量谱计算出故障频段内的能量,用此能量值与转子A相电流幅值的比值作为故障特征量。此方法提取出的故障特征量不受转子电流幅值的影响,具有很好的灵敏度与可靠性。Monitoring on doubly-fed induction generator rotor windings asymmetrical faults can effectively re-duce the probability of the motor failure , and the costs of wind farm operation and maintenance.In this pa-per, wavelet decomposition and wavelet power spectrum were combined to extract the feature of doubly -fed induction generator rotor windings asymmetrical faults.And built the experimental platform of rotor faults di-agnosis of DFIG in laboratory , a series resistance in A phase was used to simulate the rotor winding asymmet -ric faults.By means of the wavelet decomposition of rotor current , found out the fault band , and then calcu-lated the fault frequency band energy , used the ration of this energy to current of phase A as the fault fea-ture.This fault feature has nothing to do with the rotor current , and can be very sensitive and reliable.
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