基于长短期记忆神经网络的检修态电网暂态稳定评估方法

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Abstract With the expansion of power grid scale and the increase of power components, the maintenance methods of power system become more and more complex. It is difficult to evaluate the transient stability risk of power grid under massive maintenance only by traditional methods. To solve this problem, a risk assessment method of transient stability in maintenance power network based on long short term memory (LSTM) neural network is proposed. Firstly, the unified coding method of power system maintenance mode is proposed, so that the computer can quickly and accurately identify the operation state of power grid under various maintenance modes. Then, a long short term memory neural network is established and trained based on a large number of fault samples of the power grid under maintenance. After that, the accurate evaluation of the power grid transient stability under different maintenance modes is realized. Finally, a regional power grid in Central China is taken as an example to verify the accuracy of the proposed method.

Key words： power system maintenance mode transient stability long short term memory (LSTM) neural network

Received: 29 November 2022

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	Articles by authors
	WANG Buhua
	ZHU Shaoxuan
	XIONG Haoqing
	XIE Yan
	LI Xiaomeng

Cite this article:

WANG Buhua,ZHU Shaoxuan,XIONG Haoqing等. Assessment method of transient stability for maintenance power system based on long short term memory neural network[J]. Electrical Engineering, 2023, 24(1): 29-35.

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http://dqjs.cesmedia.cn/EN/Y2023/V24/I1/29

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