Stability Enhancement of Inverter-Based Microgrids Using Optimized Neural Networks - PANG Kai, TANG Zhiyuan, GAO Hongjun, LIU Youbo, LIU Junyong (Dianli jianshe, 2025)
[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.