基于积分强化学习的构网型VSC综合频率控制

doi:10.19595/j.cnki.1000-6753.tces.250670

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Abstract The voltage source converter based high voltage direct current (VSC-HVDC) systems featuring grid-forming (GFM) control can provide critical frequency and voltage support to weak or passive grids, making them a key research focus in modern power systems. This paper proposes a model-free integrated frequency control method for grid-forming voltage source converters (GFM-VSCs) within a virtual synchronous generator (VSG) framework. The method integrates the integral reinforcement learning algorithm-based optimal frequency control with active load disturbance compensation to deliver reliable frequency support to the target AC grid.
Firstly, the dynamic models of the target AC grid and the GFM-VSC were established, and the oscillatory modes present in their coupled operation were analyzed. Subsequently, an optimal frequency controller was designed based on a predefined quadratic performance index to improve system damping, independent of the VSG parameters. To address unknown model dynamics, a policy iteration algorithm utilizing impulse perturbation excitation was developed under an integral reinforcement learning framework, to solve the optimal frequency controller in a fully data-driven manner. Furthermore, a reduced order load disturbance observer was designed, along with a disturbance compensation controller. This ancillary disturbance compensation module was used to enhance the VSC's active response to load disturbances, enable rapid secondary frequency regulation, and operate in coordination with the optimal frequency controller trained by the policy iteration algorithm.
The performance of the proposed method was evaluated using an equivalent system test model and the IEEE standard 9-Bus test model, both implemented in MATLAB/Simulink. Comparative analysis included three alternative approaches: the VSG with a Nichols-PI regulator, the VSG with a V²-P-w droop regulator, and the traditional VSG scheme. During online training, the Critic neural network parameters converged within 10 iterations. The proposed impulse perturbation excitation method reduced the error norm of the Critic parameter matrix to below 0.24, demonstrating higher accuracy than traditional noise based excitation method, which achieved only 1.64. Under a 50 MW step load disturbance, the integrated frequency control method limited frequency deviations to within 0.001 p.u. in the equivalent system and 0.0015 p.u. in the IEEE 9-Bus system. The grid frequency was restored to the nominal value within 1.5 s. The proposed method exhibited significantly better frequency support performance than the three benchmark methods. It also provided faster rate of change of frequency (RoCoF) attenuation and notably suppressed frequency and power oscillations. Under a heavy load test scenario (exceeding 85% of the rated power), the method successfully restricted the active power reference command to the predefined safety limit. This ability protected the VSC from overload beyond safe durations.
Based on the simulation analysis, the following conclusions can be drawn: (1) The policy iteration algorithm incorporating impulse perturbation excitation demonstrates accelerated convergence rate and enhanced precision compared to the traditional noise based excitation methods. (2) The proposed integrated frequency control method demonstrates superior damping performance under load disturbances, significantly attenuating RoCoF, as well as frequency and power oscillations. (3) The proposed integrated frequency control method effectively compensates load-side disturbances and ensures prompt restoration of the grid frequency to its nominal value.

Key words： Voltage source converter based high voltage direct current (VSC-HVDC) grid-forming control virtual synchronous generator integral reinforcement learning load disturbance compensation

Received: 22 April 2025

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TM732

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	Li Yinsheng
	Wang Bing
	Chen Yuquan
	Wang Wancheng

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Li Yinsheng,Wang Bing,Chen Yuquan等. Integrated Frequency Control for Grid-forming VSC Based on Integral Reinforcement Learning[J]. Transactions of China Electrotechnical Society, 0, (): 250670-.

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