Neural Network-based Multi-parameter Collaborative Adaptive Optimization Control Strategy for GFVSG

WANG Xu; JIA Yan; GUO Qiang; GUO Mingyang

doi:10.13296/j.1001-1609.hva.2025.11.009

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Neural Network-based Multi-parameter Collaborative Adaptive Optimization Control Strategy for GFVSG

Parameter Optimization Design | 更新时间：2025-11-04

- Neural Network-based Multi-parameter Collaborative Adaptive Optimization Control Strategy for GFVSG
- High Voltage Apparatus Vol. 61, Issue 11, Pages: 98-108(2025)
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- DOI：10.13296/j.1001-1609.hva.2025.11.009
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- Received：09 May 2025，
  
  Revised：2025-07-20，
  
  Published：16 November 2025
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WANG Xu, JIA Yan, GUO Qiang, et al. Neural Network-based Multi-parameter Collaborative Adaptive Optimization Control Strategy for GFVSG[J]. High voltage apparatus, 2025, 61(11): 98-108.
DOI：

WANG Xu, JIA Yan, GUO Qiang, et al. Neural Network-based Multi-parameter Collaborative Adaptive Optimization Control Strategy for GFVSG[J]. High voltage apparatus, 2025, 61(11): 98-108. DOI： 10.13296/j.1001-1609.hva.2025.11.009.

摘要

构网型虚拟同步发电机（GFVSG）可以为电网提供主动支撑，但存在有功频率响应慢和频率抗扰动能力差的问题。文中借助GFVSG参数灵活可调的优势，提出一种基于神经网络的GFVSG多参数协同自适应优化控制策略。首先，建立数学模型，解析转动惯量与阻尼系数对系统的动态影响机理，其次，结合同步发电机功角特性与频率振荡曲线，归纳出参数的动态整定准则与实时需求；最后，引入强非线性映射能力的RBF神经网络，构建参数自整定框架，设计复合误差函数，引入动态耦合系数，实现多参数协同自适应优化控制。仿真结果表明，所提策略在指令功率突变时可有效抑制输出功率和频率的超调与振荡，兼具响应速度；且在网侧故障工况下能够提供更强的惯量响应支撑。

Abstract

Grid-forming virtual synchronous generator（GFVSG）can provide active support to the power grid，yet it has suh problems as slow active-power response and weak frequency interference resistance ability. In this paper， with the advantage of flexible and adjustable GFVSG paramater，a multi-parameter cooperative adaptive optimization control strategy based on neural networks is proposed. First，a mathematical model is set up to analyze the dynamic influence mechanism of rotational inertia and damping coefficient on system. Then，by integrating the synchronous generator power-angle characteristics with the frequency oscillation trajectory，dynamic tuning rules and real-time parameter requirements are derived. Finally，an RBF neural network with strong nonlinear mapping capability is introduced to construct a self-tuning framework，design composite error fucntion，introduce dynamic dynamic coupling coefficients and achieve cooperative self adaptive optimizatino control of multiple parameters. The simulation results show that，in case of sudden change of power command，the proposed strategy can effectively suppresses overshoot and oscillation in both output power and frequency，have response speed and，in case of fault at the grid side，can provide strnger inertia response support.

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references

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