RBF-Based Adaptive Control Strategy of Rotational Inertia and Damping Coefficient for VSG

doi:10.12204/j.issn.1000-7229.2022.09.014

Abstract

Abstract:

The virtual synchronous generator (VSG) control strategy enables the power electronic converter to have the moment of rotational inertia and damping coefficient of the synchronous generator, but the relationship between the two parameters and the frequency in the regulation process is a nonlinear function, which is regarded as a linear relationship in the traditional methods. The control strategy can only roughly adjust two parameters and the adjustment frequency is too high. Therefore, this paper proposes an adaptive control strategy of inertia and damping for VSG, which is based on Radial Basis Function (RBF), using the artificial intelligence algorithm to improve the control strategy. Firstly, this paper analyzes the control methods of moment of inertia and damping coefficient from the perspectives of VSG mathematical model, output characteristics and small signal model, and gives the value ranges of corresponding parameters. Secondly, according to the unique nonlinear relationship of VSG, a double-input and double-output RBF neural network control strategy is established. Finally, the transient response of the traditional control strategy and the control strategy proposed in this paper are compared by Matlab / Simulink simulation to verify the effectiveness of the proposed control strategy.

Key words: virtual synchronous generator, frequency response, rotational inertia, damping coefficient, neural network, adaptive control strategy

CLC Number:

TM711

GAO Zixuan, ZHAO Jinbin, YANG Xuhong, YAO Fengjun, FANG Jianfeng. RBF-Based Adaptive Control Strategy of Rotational Inertia and Damping Coefficient for VSG[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(9): 132-139.

Figures/Tables 14

Fig.1 VSG control diagram based on RBF neural network

Fig.2 Active power and reactive power control loops of VSG

Fig.3 Equivalent circuit of grid-connected VSG

Fig.4 Dynamic responses of active power output under different rotational inertias and damping cofficients

Fig.5 Variation of evaluation index of system angular frequency under different rotational inertia and damping coefficient

Fig.6 RBF neural network structure diagram for VSG control

Fig.7 Flowchart of RBF-JD control

Table 1 Main parameters of the simulation model

Table 2 Main parameters of the adaptive linear control strategy

Table 3 Main parameters of RBF-J algorithm

Fig.8 Comparison of active power under different control strategies

Table 4 Analysis indicators of different control strategies in two scenarios

Fig.9 Comparison of angular frequency under different control strategies

Fig.10 Comparison of rotational inertia and damping coefficient under different control strategies

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