电力建设2025，Vol.46Issue(8)：67-77,11.DOI:10.12204/j.issn.1000-7229.2025.08.007
基于优化神经网络的微电网稳定性提升策略
Stability Enhancement of Inverter-Based Microgrids Using Optimized Neural Networks
摘要
Abstract
[Objective]With the increasing penetration of power electronic devices,such as energy storage and photovoltaics,in microgrids,their low inertia and low damping characteristics pose challenges to the stable operation of microgrids(MGs).To enhance the stability of inverter-based MGs,this study introduces a novel data-driven method for the coordinated and rapid local adjustment of inverter multicontrol parameters.[Methods]An offline eigenvalue-based optimization problem was formulated to compute the optimal multicontrol parameters using the osprey optimization algorithm(OOA)under various operating conditions.Subsequently,to minimize the reliance on global system information,a multilabel feature selection algorithm is employed to identify the most relevant local measurements that influence the adjustment of each control parameter.Finally,local measurements are treated as input variables and optimal control parameters as output variables.A novel deep learning algorithm based on northern goshawk optimization(NGO)and a bidirectional gated recurrent unit(BiGRU)is proposed to train the local parameter optimization model(LPOM)by learning the input-output mapping.[Results]The case study demonstrates that the designed LPOM can swiftly adjust controller parameters based on online measurement data,thereby enhancing microgrid stability.It also establishes that the proposed deep learning algorithm achieves higher accuracy in training the LPOM compared to traditional neural networks.The LPOM delivers faster computation speeds for parameter optimization.[Conclusions]The proposed method only requires local measurement data and rapidly enhances the small-signal stability of microgrids through online dynamic optimization of multiple inverter control parameters.关键词
小干扰稳定性/微电网/数据驱动/北方苍鹰优化算法(NGO)/双向门控循环单元(BiGRU)Key words
small-disturbance stability/microgrid/data-driven/northern goshawk optimization(NGO)/bidirectional gated recurrent unit(BiGRU)分类
信息技术与安全科学引用本文复制引用
庞凯,唐志远,高红均,刘友波,刘俊勇..基于优化神经网络的微电网稳定性提升策略[J].电力建设,2025,46(8):67-77,11.基金项目
国家自然科学基金项目(52207127) （52207127）
中央高校基本科研业务费专项资金资助项目(YJ2021163) This work is supported by National Natural Science Foundation of China(No.52207127)and Fundamental Research Funds for the Central Universities(No.YJ2021163). （YJ2021163）
