基于卷积神经网络的电力系统小干扰稳定评估与预防控制

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Abstract A small-signal stability preventive control method based on convolutional neural network (CNN) sensitivity analysis is presented in the paper, to improve the developing speed of small- signal stability preventive control measures. For poor or negative damping low frequency oscillation modes (i.e., the damping ratios are smaller than a threshold), first, an optimization model with small- signal stability constraints is established; second, the sensitivities of the damping ratios with respect to control variables (the active power of adjustable generators) based on CNN model of damping ratio prediction are calculated and then the optimization model is transformed into a quadratic programming model by linearizing small-signal stability constraints through sensitivities; finally, the adjustment amounts of generator active power are obtained. Several iterations are needed to make the damping ratios meet specific requirements. Analysis results of WEPRI 36-node case show that the effective control measures can be obtained by the presented method, which is more precise than that of the support vector machine method. The computing speed of the presented method is faster than that of the traditional eigenvalue analysis method. The ideas presented in this paper can also be applied to transient stability preventive control.

Key words： convolutional neural network (CNN) sensitivity analysis small-signal stability stability assessment preventive control

Received: 04 September 2024

About author:: 国家自然科学基金项目（U21666601）

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	TIAN Fang
	ZHOU Xiaoxin
	YU Zhihong

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TIAN Fang,ZHOU Xiaoxin,YU Zhihong. Small-signal stability assessment and preventive control of power system based on convolutional neural network[J]. Electrical Engineering, 2025, 26(3): 1-6.

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https://dqjs.cesmedia.cn/EN/Y2025/V26/I3/1

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