Estimation Approach for Inertia of Power System Based on Variable Data Window

doi:10.16183/j.cnki.jsjtu.2024.339

Abstract

Abstract:

Due to the ineffectiveness of fixed data window caused by time-varying frequency dynamics, an estimation for inertia of power systems based on variable data window is proposed. First, the process of power system inertia response is analyzed, and the auto-regressive with extra inputs model is used to depict frequency dynamics, on which a parameter identification model is developed. Next, the frequency measurement data are preprocessed based on the adaptive median-mean combined filter, and the fuzzy logic controller is used to set the parameters of the forgetting factor recursive least square method. Then, indices measuring the identification error and the difference of the results of successive windows are developed to realize the online variation of data window length and calculate the system inertia. Finally, case studies are conducted to validate the effectiveness of the proposed approach. The results show that the proposed approach can adapt to the changing inertia and is effective in reducing the inertia estimation error.

Key words: equivalent inertia, frequency dynamic, parameter identification, online estimation, variable data window

CLC Number:

TM715

CAO Yongji, LI Changgang. Estimation Approach for Inertia of Power System Based on Variable Data Window[J]. Journal of Shanghai Jiao Tong University, 2026, 60(4): 541-549.

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ΔE	E
ΔE	NB	NM	NS	ZO	PS	PM	PB
NB	PB	PB	PM	PM	PS	ZO	NS
NM	PB	PB	PM	PS	PS	NS	NS
NS	PB	PM	PM	PS	PS	NS	NS
ZO	PM	PM	PS	ZO	ZO	NM	NM
PS	PS	PS	ZO	NS	NM	NM	NB
PM	PS	PS	NS	NS	NM	NB	NB
PB	PS	PS	NS	NS	NM	NB	NB

方法	RMSE	MAE	NRP/%
方法1(中值-均值滤波方法)	5.0220	0.0290	87.35
方法2(带修正因子的中值- 均值滤波方法)	4.2632	0.0246	91.29
所提方法	2.9536	0.0171	95.13

方法	RMSE	MAE	ME
方法3(ARMAX模型方法)	0.0304	0.0242	0.1235
方法4(固定数据窗口方法)	0.0276	0.0220	0.1143
所提方法	0.0148	0.0118	0.0581

方法	时间区间 0~200 s		时间区间 200~300 s
方法	惯量估计值/s	惯量估计误差/%	惯量估计值/s	惯量估计误差/%
方法3(ARMAX模型方法)	7.11	11.31	8.12	14.33
方法4(固定数据窗口方法)	5.94	6.92	6.51	8.37
所提方法	6.23	2.47	6.92	2.53