A Method of Locating the Power System Subsynchronous Oscillation Source Unit with Grid-Connected PMSG Using Deep Transfer Learning
Chen Jian1, Du Wenjuan2, Wang Haifeng1,2
1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China; 2. College of Electrical Engineering Sichuan University Chengdu 610065 China
Abstract：With increasing penetration of new energy power electronic devices, the induced mechanism of the subsynchronous oscillation problem of power systems has become more complicated. In order to locate the unit that induces subsynchronous oscillation in time, a method for locating sub-synchronous oscillation sources based on deep transfer learning is proposed. The method first builds a simulation system based on the open-loop mode resonance theory, and obtains training data samples in the simulation system. Second, it uses convolutional neural networks(CNN) to extract the characteristics of the oscillation source and establish a training localization model. Finally, the training model is transferred to the actual situation through transfer learning, and to realize the application of the location model. In order to verify the effectiveness of the proposed method, this paper designs a simulation system example of a power system with direct-drive generators connected to the grid. The test results show that the proposed method has the advantages of high location accuracy and convenient online application, compared with the traditional eigenvalue analysis method. Besides, this method can give the identification result in a short time, which lay a foundation for realizing the online identification of the oscillation source.
陈剑, 杜文娟, 王海风. 采用深度迁移学习定位含直驱风机次同步振荡源机组的方法[J]. 电工技术学报, 2021, 36(1): 179-190.
Chen Jian, Du Wenjuan, Wang Haifeng. A Method of Locating the Power System Subsynchronous Oscillation Source Unit with Grid-Connected PMSG Using Deep Transfer Learning. Transactions of China Electrotechnical Society, 2021, 36(1): 179-190.
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2021, Vol. 36 
