Prediction of the lowest system frequency when disturbance occurs based on power deficit estimation

Xingyuan Wei

doi:10.1117/12.3030297

5 June 2024 Prediction of the lowest system frequency when disturbance occurs based on power deficit estimation

Xingyuan Wei

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131634P (2024) https://doi.org/10.1117/12.3030297
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

After a disturbance occurs in the power system, predicting the minimum frequency is a crucial aspect of frequency security and stability analysis in power systems. This paper proposes a model for estimating system power deficit following a disturbance to predict the minimum frequency in power systems. Building upon the traditional average frequency response model, the model calculates the frequency variation rates for each generator 2 ms after a disturbance to estimate the power deficit for each generator. The entire system's generator inertia constant (H) and power deficit (P) are equivalently represented. Utilizing the governor model and rotor motion equations, a mathematical model for frequency prediction is developed. Finally, based on the boundary conditions at the moment of the appearance of the minimum frequency, the minimum frequency is determined. Simulation results from a three-machine nine-bus system in PowerFactory are compared, demonstrating that the proposed algorithm can rapidly and accurately compute the minimum frequency after a disturbance occurs in the system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingyuan Wei "Prediction of the lowest system frequency when disturbance occurs based on power deficit estimation", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131634P (5 June 2024); https://doi.org/10.1117/12.3030297

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