A Fast Prediction Method of Coherent Generators Based on Long Short-term Memory Network

doi:10.11985/2022.02.023

Abstract

Abstract:

Based on the long short-term memory network(LSTM), a fast prediction method of coherent generators is proposed. Firstly, the classification characteristics of bus voltage phase trajectories are extracted to provide a new way for the identification of generator coherency. Secondly, based on the short-term response data, the real and imaginary parts of the generator terminal voltage phase are predicted respectively by using LSTM, and the coherent generators are identified based on the fitted voltage phase trajectories. Finally, the extended equal area criterion(EEAC) is used to further verify the coherency of the identified generator groups. The proposed method is validated used in the IEEE-39 bus system, and the simulation results show that the method has the advantages of higher engineering practice value.

Key words: Coherent generator identification, voltage phase trajectory, long short-term memory network(LSTM), extended equal area criteria(EEAC)

CLC Number:

TM561

MAO Yu, SHANG Haikun, YU Zhuoqi. A Fast Prediction Method of Coherent Generators Based on Long Short-term Memory Network[J]. Journal of Electrical Engineering, 2022, 17(2): 201-207.

Figures/Tables 14

References 18

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暂态稳定裕度	同调辨识结果
η≥1	S和A具有强同调性
η<1	S和A分为两群

神经元参数	描述
激活函数	sigmoid
权值初始化	正态
损失函数	方均根误差函数
优化算法	RMSProp
Batchsize	15