Fault anomaly detection of synchronous machine winding based on isolation forest and impulse frequency response analysis

•This method adopts unsupervised learning and does not need to label the experimental data.•The method proposed in this paper is more suitable for data structures in real life.•A winding fault detection method of synchronous machine based on isolated forest and IFRA is proposed, which overcomes the shortcomings of traditional methods, such as long time-consuming, low accuracy, demand for massive fault experimental data. Synchronous machine is one of the critical power generation parts in the power system. Its stable operation ensures people's normal economic activities. Winding is an essential component of a synchronous machine, and the winding fault is a common fault type. The reliable and efficient fault diagnosis of synchronous machine winding is of great significance to ensure the stability of the power system. Therefore, this paper proposes an anomaly detection method of synchronous machine winding fault based on isolation forest (IF) and impulse frequency response analysis (IFRA). Firstly, the basic principle of the anomaly detection method is introduced, and mathematical-statistical indicators of IFRA signatures used are then explained. Besides, the experimental verification is carried out on a 5 kW synchronous machine, and the performance of the anomaly detection method for winding fault is compared with other conventional methods. The experimental results show that the proposed method is feasible and effective, and the generalization ability of the data is strong. The comparative experimental results show that the proposed method is superior to the existing conventional supervised learning method. It has a shorter calculation time and higher accuracy, with stronger robustness, which is more suitable for the actual data structure.