To probe an accurate diagnosing approach for synchronous generator (SG) with rotor winding inter-turn short-circuit, a novel online monitoring and detecting method relying on the ν-support vector regression (ν-SVR) machine was proposed, and its effectiveness was further verified by the micro-synchronous generator dynamic simulation. Terminal voltage, active and reactive power of SG were selected as input variables for a novel prediction model based on the ν-SVR, and field current was selected as an output variable of the prediction model. The structures and parameters of the field current prediction model were optimized with the particle swarm optimization (PSO) algorithm and training samples, then the prediction model was established and the field current prediction got under way. By comparing the predicted field current with the corresponding online measured field current, inter-turn short-circuit of rotor winding in SG could be detected sensitively once its absolute value of the prediction relative error exceeded a specific threshold. The micro-synchronous generator dynamic simulation indicated that the proposed online detecting approach based on the ν-SVR machine overcame the shortage of the back-propagation (BP) diagnosis method for misdiagnosis, and its accuracy, sensitivity and threshold setting range of the diagnosis method was the most prominent among these diagnosis methods such as the BP diagnosis method, the Bayesian regularization back-propagation (BRBP) diagnosis method and the ε-support vector regression (ε-SVR) diagnosis method.

中文翻译：

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基于ν-SVR机的发电机转子绕组匝间短路在线检测

为探索同步发电机（SG）转子绕组匝间短路的准确诊断方法，一种新型的在线监测检测方法ν-支持向量回归（ν提出了-SVR)机，并通过微同步发电机动态仿真进一步验证了其有效性。选择 SG 的端电压、有功和无功功率作为输入变量，用于基于ν-SVR，选择励磁电流作为预测模型的输出变量。采用粒子群算法和训练样本对磁场电流预测模型的结构和参数进行优化，建立预测模型并进行磁场电流预测。通过将预测的励磁电流与相应的在线测量的励磁电流进行比较，一旦其预测相对误差的绝对值超过特定阈值，就可以灵敏地检测到SG中转子绕组的匝间短路。微同步发电机动态仿真表明，所提出的在线检测方法基于ν-SVR机克服了反向传播（BP）诊断方法误诊的不足，其诊断方法的准确性、灵敏度和阈值设定范围在BP诊断方法、贝叶斯正则化等诊断方法中最为突出反向传播（BRBP）诊断方法ε-支持向量回归（ε-SVR) 诊断方法。