Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2023 , Vol. 57 >Issue 4: 403 - 411

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.401

New Type Power System and the Integrated Energy

Online Estimation of Power Shortage in Power Systems Driven by Local Frequency Measurement Data

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  • 1. Electric Power Research Institute, State Grid Shanxi Electric Power Company, Taiyuan 030001, China
    2. China Electric Power Research Institute, Beijing 100192, China

Received date: 2021-10-12

  Revised date: 2022-04-05

  Accepted date: 2022-04-25

  Online published: 2023-01-06

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Abstract

With the growing penetration of renewable generation, the inertia and frequency support ability of the renewable-dominated power system are continuously reduced, which leads to frequency collapse when the system is disturbed. Therefore, it is of great significance to promptly and accurately evaluate the power shortage after a major disturbance to fill the power shortage quickly. This paper proposes an online estimation approach of power shortage in power system based on deep convolution and long-short term memory composite neural network driven by local frequency measurement data. First, since using the synchronous measurements to obtain the frequency of center of inertia (COI) cannot adapt to the rapidity of online estimation, this paper employs the local frequency measurements to estimate the COI frequency, avoiding the delay effect caused by the complex communication. Then, it designs a deep composite neural network to mine the correlation information between massive frequency data and power shortage. Finally, it tests the simulations on a 39-bus system to verify the effectiveness of the proposed approach. The results demonstrate that the proposed approach is effective and fast.

Key words： renewable-dominated power system; frequency response; composite neural network; center of inertia (COI); data-driven

Cite this article

TANG Zhen, HAO Lihua, FENG Jing . Online Estimation of Power Shortage in Power Systems Driven by Local Frequency Measurement Data[J]. Journal of Shanghai Jiaotong University, 2023 , 57(4) : 403 -411 . DOI: 10.16183/j.cnki.jsjtu.2021.401

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