Area Division of Power Grid and Inertia Online Identification Method Based on Routine Disturbance Data

Yixuan CHEN , Shuaishuai FENG , Lingfang LI , Guangzeng YOU , Peng SUN , Deping KE , Weiyang ZHAO

›› 2023, Vol. 17 ›› Issue (7) : 83 -94.

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›› 2023, Vol. 17 ›› Issue (7) : 83 -94. DOI: 10.13648/j.cnki.issn1674-0629.2023.07.010
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Area Division of Power Grid and Inertia Online Identification Method Based on Routine Disturbance Data

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Abstract

Considering that power system inertia may fluctuate with the change of operation scenario due to large-scale access to renewable energy plants, it is necessary to monitor the equivalent power system inertia for the stable operation of the power grid. Therefore, a set of area devision inertia identification method is proposed based on routine disturbance data. Firstly, the algorithm of total least square estimation of signal parameters via rotational invariance techniques is used to extract the oscillation components of different bus frequncy, and the power gird will be divided according to the extracted results. In this way, the signal to noise ratio (SNR) of the monitoring results of the power fluction between different regions is improved. Then, the subspace identification algorithm is used to identify the dynamic relationship between the area center of inertia frequency and unbalanced power. Futher, area division inertia will be calculate based on the above results. Finally, the effectiveness of the proposed inertia identification method based on the area division is verified in the IEEE 39-bus system.

Keywords

area division of power grid / subspace identification / estimation of signal parameters / routine disturbance data / inertia identification

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Yixuan CHEN,Shuaishuai FENG,Lingfang LI,Guangzeng YOU,Peng SUN,Deping KE,Weiyang ZHAO. Area Division of Power Grid and Inertia Online Identification Method Based on Routine Disturbance Data. 2023, 17(7): 83-94 DOI:10.13648/j.cnki.issn1674-0629.2023.07.010

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Funding

the Science and Technology Project of Yunnan Power Grid Co., Ltd(YNKJXM20200165)

the National Natural Science Foundation of China(51777143)

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