张浩雅,邵文权,吴成锋,杨鹏
ZHANG Haoya,SHAO Wenquan,WU Chengfeng,YANG Peng

• 1:西安工程大学电子信息学院
• 2:国网安徽省电力有限公司东至县供电公司

摘要(Abstract)：

电网功率扰动引发转动惯量与阻尼系数动态耦合失调，导致传统光储VSG（虚拟同步发电机）存在有功超调及频率波动大的问题。提出一种基于EWOA（增强鲸鱼优化算法）与RBFNN（径向基函数神经网络）的光储VSG惯量与阻尼自适应控制策略。结合VSG数学模型与小信号模型，分析惯量及阻尼参数的调节方法及其取值范围。通过引入动态参数调整及精英个体指导机制，基于EWOA实现对RBF（径向基函数）权值的全局优化，提升网络对非线性系统的逼近精度与泛化能力。优化后的RBFNN可实时调节VSG惯量与阻尼参数，实现系统动态特性的自适应控制。仿真验证表明，该策略能够有效抑制有功超调及频率偏差，尽管频率波动略有增加，但频率超调量控制在0.5%以内，满足系统运行要求；同时有效缩短系统稳定时间，提升暂态响应性能和系统动态稳定性。
Power disturbances in the grid induce dynamic coupling imbalances between inertia and damping coefficients, resulting in active power overshoot and significant frequency fluctuations in conventional photovoltaicstorage Virtual Synchronous Generators(VSGs). This paper proposes an adaptive control strategy for inertia and damping of photovoltaic-storage VSGs based on an Enhanced Whale Optimization Algorithm(EWOA) combined with a Radial Basis Function(RBF) neural network. By integrating the VSG mathematical and small-signal models, the methods for adjusting inertia and damping parameters and their feasible ranges are analyzed. The EWOA enhances global optimization of RBF weights through dynamic parameter adaptation and elite individual guidance mechanisms, thereby improving the network's approximation accuracy and generalization capability for nonlinear systems. The optimized RBF neural network dynamically adjusts the VSG's inertia and damping parameters in real time to achieve adaptive control of system dynamic characteristics. Simulation results demonstrate that the proposed strategy effectively suppresses active power overshoot and frequency deviation; although frequency fluctuation slightly increases, the frequency overshoot remains within 0.5%, meeting operational requirements. Moreover, the approach significantly shortens system settling time and enhances transient response performance and overall dynamic stability.

关键词(KeyWords)： 虚拟同步发电机;虚拟惯量;虚拟阻尼系数;RBFNN;EWOA;自适应控制
virtual synchronous generator;virtual inertia;virtual damping coefficient;RBF neural network;WOA whale optimization algorithm;adaptive control

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（52407137）;; 新型电力系统运行与控制全国重点实验室开放基金（SKLD24KM03）

作者(Author): 张浩雅,邵文权,吴成锋,杨鹏
ZHANG Haoya,SHAO Wenquan,WU Chengfeng,YANG Peng

DOI: 10.19585/j.zjdl.202601008

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