管敏渊,姚瑛,吴圳宾,满敬彬,吴伟强
GUAN Minyuan,YAO Ying,WU Zhenbin,MAN Jingbin,WU Weiqiang

• 1:国网浙江省电力有限公司湖州供电公司
• 2:浙江泰仑电力集团有限责任公司
• 3:上海电力大学电气工程学院
• 4:国网浙江长兴县供电有限公司

摘要(Abstract)：

针对传统储能VSG（虚拟同步发电机）不能较好地同时具备抗扰动能力和快速动态响应能力的问题，提出一种以RBF（径向基函数）神经网络优化动态同步器的储能VSG控制策略。首先，建立VSG的数学模型，分析转动惯量和阻尼系数配置对VSG性能的影响，得出参数配置在动态响应和系统动态稳定的矛盾关系。其次，将转子的暂态不平衡功率作为三层前向结构RBF神经网络算法的输入，通过RBF神经网络算法在线学习得出最优暂态补偿功率来动态调节VSG的输入功率，从而减少转子的不平衡转矩，提高VSG的暂态稳定性。最后，通过仿真对比实验验证了所提控制策略的有效性。
In response to the issue that traditional energy storage VSGs(virtual synchronous generators) cannot simultaneously possess good disturbance resistance and rapid dynamic response capabilities, a control strategy for energy storage VSGs is proposed, optimizing the dynamic synchronizer using RBF(radial basis function) neural networks. First, a mathematical model for VSG is established, analyzing the impact of rotor inertia and damping coefficient configuration on VSG performance. This analysis reveals the conflicting relationship between parameter configuration and dynamic response versus system dynamic stability. Subsequently, the transient unbalanced power of the rotor is taken as input for a three-layer forward structure RBF neural network algorithm. Through online learning with the RBF neural network algorithm, the optimal transient compensation power is obtained to dynamically adjust the input power of VSG, thereby reducing unbalanced rotor torque and enhancing the transient stability of VSG. Finally, simulation and comparative experiments are conducted to validate the effectiveness of the proposed control strategy.

关键词(KeyWords)： 虚拟同步机控制;RBF神经网络;同步器动态控制;储能逆变器;暂态稳定
virtual synchronous generator control;RBF neural network;dynamic synchronizer control;energy storage inverter;transient stability

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司集体企业科技项目（2019-HUZJTKJ-19）;; 国家重点研发计划（2022YFB2404300）

作者(Author): 管敏渊,姚瑛,吴圳宾,满敬彬,吴伟强
GUAN Minyuan,YAO Ying,WU Zhenbin,MAN Jingbin,WU Weiqiang

DOI: 10.19585/j.zjdl.202403007

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