Online Generator Excitation Current Estimation Based on Model-Data Hybrid Drive
Liu Mulin1, Jiang Tong1, Xu Yongjin2, Wang Rui3
1. Skate Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China; 2. GE (Shanghai) Power Technology Co. Ltd Beijing 100600 China; 3. Huainan Pingwei Electric Power Generating Co. Ltd Huainan 232089 China
Abstract：Monitoring excitation current is essential for synchronous generators, especially huge turbo generators. By observing trends in excitation current, plant personnel can promptly assess the generator's operational status. Moreover, various internal rotor faults can be detected online by comparing the theoretically estimated excitation current with the measured values. Traditional model-based methods for calculating excitation current are fast, but the accuracy is often unsatisfactory. On the other hand, finite element simulation-based methods offer precise calculations but are too slow for online monitoring systems. This paper proposes an online estimation method based on model-data hybrid drive, leveraging the strengths of generator models and machine learning techniques. This method integrates the concept of dynamic Potier reactance into traditional online estimation methods. The reactance, modeled and estimated through machine learning, replaces the previously fixed Potier reactance value in theoretical excitation current calculations. The method has been validated through over 1 400 simulations in PSCAD and more than 4 400 experiments on a 644 MW turbo generator unit. During validation with PSCAD simulation data, the model-based method’s error ranges from 0% to -6% between theoretical and measured excitation currents, while the proposed method’s error is within ±1% with a minimal exceeding ±2%. Similarly, operational unit data show the error ranges from -3% to -5% for the model-based method, and the proposed method's error is predominantly within ±1.3% with rare deviations of about 2%. Additionally, the average time of this method is 113 milliseconds, meeting the real-time requirements of online systems. Finally, fault diagnosis experiments are conducted for 499 inter-turn short-circuit fault conditions of the same generator, confirming the feasibility and effectiveness of this method for online fault detection. The following conclusions are drawn. (1) Compared to the model-based online excitation current estimation methods, the proposed method introduces dynamic Potier reactance, fully considering the saturation and distortion of the generator’s internal magnetic field. The accuracy of theoretical excitation current estimation under a wide range of operating conditions is enhanced. (2) The input to the machine learning prediction model only consists of basic measured data from the synchronous generator (including stator voltage, stator current, and power factor angle). In contrast, the finite element simulation-based methods require numerous parameters and substantial computational resources. This method offers excellent adaptability and computational speed. Additionally, the overall operation time is very short, meeting the real-time requirements of online systems. (3) The data containing magnetic field characteristics is input into the dynamic Potier reactance model to extract features. This method better reflects the relationship between excitation current variations and generator operating conditions machine learning models. Accurate theoretical excitation current estimation can also facilitate online detection of rotor inter-turn short-circuit faults without additional detection equipment. Furthermore, it can be combined with other data to analyze the generator’s operational status.
刘沐霖, 姜彤, 徐永金, 王锐. 基于模型-数据混合驱动的同步电机励磁电流在线估计方法[J]. 电工技术学报, 2025, 40(18): 5866-5876.
Liu Mulin, Jiang Tong, Xu Yongjin, Wang Rui. Online Generator Excitation Current Estimation Based on Model-Data Hybrid Drive. Transactions of China Electrotechnical Society, 2025, 40(18): 5866-5876.
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2025, Vol. 40 
