| 引用本文: | 梁展豪,曾 君,刘俊峰.考虑出力连续波动的虚拟同步机自适应惯量阻尼控制策略[J].电力系统保护与控制,2025,53(23):1-12.[点击复制] |
| LIANG Zhanhao,ZENG Jun,LIU Junfeng.Adaptive inertia damping control strategy for virtual synchronous generators considering continuous output fluctuations[J].Power System Protection and Control,2025,53(23):1-12[点击复制] |
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| 摘要: |
| 应用虚拟同步机(virtual synchronous generator, VSG)技术的可再生能源发生出力波动时，通过自适应惯量阻尼控制可以抑制由VSG固有动态响应特性导致的输出功率、频率附加振荡。但现有的基于径向基函数(radial basis function, RBF)的VSG自适应控制策略控制灵活性不足且参数配置复杂，难以在出力连续波动下有效抑制输出功率、频率振荡。针对这一问题，提出了一种基于粒子群优化(particle swarm optimization, PSO)算法优化RBF神经网络的VSG自适应惯量阻尼控制策略。首先，对VSG进行小信号建模，确定虚拟惯量、虚拟阻尼的取值范围并设定稳态惯量。然后，利用RBF神经网络对VSG角频率与虚拟惯量、虚拟阻尼之间的关系进行拟合，并引入J惯性因子项扩展RBF神经网络的控制维度。进一步，通过改进PSO对RBF神经网络超参数进行优化配置，提高RBF神经网络的拟合及泛化能力，使其在面临复杂的功率、频率波动时能够进行自适应惯量阻尼调节。最后，搭建VSG并网模型，在出力突变、出力连续波动、不同电网条件3种工况下验证了所提控制策略的可行性及优越性。 |
| 关键词: 虚拟同步机 出力连续波动 径向基函数 粒子群优化 自适应惯量阻尼控制 |
| DOI：10.19783/j.cnki.pspc.250085 |
| 投稿时间：2025-01-20修订日期：2025-06-04 |
| 基金项目:国家自然科学基金项目资助(62173418，52377186)；广东省自然科学基金项目资助(2024A1515012428) |
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| Adaptive inertia damping control strategy for virtual synchronous generators considering continuous output fluctuations |
| LIANG Zhanhao1,ZENG Jun1,LIU Junfeng2 |
| (1. School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China;
2. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China) |
| Abstract: |
| When renewable energy sources using virtual synchronous generator (VSG) technology experience output fluctuations, adaptive inertia damping control can suppress the additional oscillations in output power and frequency caused by the inherent dynamic response of the VSG. However, existing VSG adaptive control strategies based on radial basis function (RBF) lack control flexibility and involve complex parameter configuration, making them ineffective at restraining output power and frequency oscillations under continuous output fluctuations. To address this issue, a VSG adaptive inertia damping control strategy is proposed, in which particle swarm optimization (PSO) is used to optimize the RBF neural network. First, a small-signal model of the VSG is established, the range of values for virtual inertia and virtual damping are determined, and the steady-state inertia is set. Then, an RBF neural network is utilized to fit the relationship between the VSG’s angular frequency and its virtual inertia and damping, and a J-inertia factor is introduced to expand the control dimension of the RBF neural network. Furthermore, an improved PSO algorithm is applied to optimize the hyperparameters of the RBF neural network, enhancing its fitting and generalization capability so that it can perform adaptive inertia damping regulation under complex power and frequency fluctuations. Finally, a VSG grid-connected model is established, and the feasibility and superiority of the proposed control strategy are verified under three operating conditions: sudden output change, continuous output fluctuations and different grid conditions. |
| Key words: virtual synchronous generator continuous output fluctuation radial basis function particle swarm optimization adaptive inertia damping control |
