An improved method of power system inertia online estimation based on system identification
DOI：DOI: 10.19783/j.cnki.pspc.201352
Key Words:power system inertia  online estimation  system identification  order of the identification model  step response
Author NameAffiliation
XU Bo School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China 
ZHANG Linwei School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China 
YU Xiangdong School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China 
BIAN Xiaoyan School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China 
LI Dongdong School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China 
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Abstract:Although the online inertia estimation method based on system identification can estimate the inertia of the power system, it is unable to determine the best model order, leading to large errors. In order to improve the accuracy of online estimation of inertia, this paper first preprocesses the measured data under the normal operating conditions of a power system to prevent over-fitting caused by noise information and to improve the ability to resist noise disturbance. Secondly, the generator's active power change is taken as input, and frequency fluctuation is taken as output. The AIC criterion is used to determine the order of the system identification model, and the system identification method is used to establish a suitable dynamic model. Then the step response of the identification model is used to calculate the inertia of the system to avoid errors caused by the reduction of the model in the inertia online evaluation process. Finally, a simulation system is built in Matlab/Simulink to verify the effectiveness and accuracy of the proposed method. This work is supported by the National Natural Science Foundation of China (No. 51977128) and the Project of Shanghai Science and Technology Commission (No. 17020500800).
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