When the reactor produces turn-to-turn insulation fault, it will cause great damage to the long-term used reactor. Turn-to-turn short circuit, as a common and frequent case of insulation failure, will cause a great disturbance to the stability of the power system if not detected in time. In this paper, a multi-factor turn-to-turn insulation short circuit fault detection method based on machine learning is proposed. This paper chooses the factor from both the time domain and the frequency domain, including both the amplitudes and the phases of vibration signal of the measured point. The experimental platform has been established to simulate the occurrence of turn-to-turn insulation fault. The performance of the generated classification model has been evaluated and discussed. Compared with the commonly used detection method, the results show that the method has higher feasibility and accuracy.

dry-type air-core reactor,insulation short circuit fault,machine learning,vibration characteristics,multi-factor