Frequency and Voltage Stabilization With Polynomial Petri Fuzzy Neural Network Based Control Strategy for Microgrid Clusters

The growing complexity of modern power systems and the increasing integration of distributed energy resources necessitate advanced control strategies for microgrid clusters (MGCs). This study investigates the adoption of polynomial petri fuzzy neural network (PPFNN) based controller in MGCs to address these challenges. The PPFNN-based controller combines the strengths of polynomial theory, Petri nets, and fuzzy neural networks, providing a robust framework for dynamic consensus and coordination among interconnected microgrids. Traditional control methods often fall short in dealing with the dynamic and stochastic nature of microgrid systems. The PPFNN-based controller, with its ability to combine the robustness of fuzzy logic and the learning capabilities of neural networks, offers a superior solution for maintaining voltage stability, frequency regulation, and efficient power sharing. This study demonstrates that adopting PPFNN-based controller not only improves the operational resilience of MGCs but also maintains voltage and frequency stability, enhances system robustness against disturbances. By leveraging the adaptive learning capabilities of neural networks and the logical structuring of Petri nets, PPFNN-based controller provides a sophisticated solution for the real-time operational demands of modern MGCs, ensuring a resilient and efficient power system. Through real-time simulation, the research highlights the controller’s effectiveness in handling various scenarios, thus providing a scalable and reliable approach to modern energy grid challenges.