Frequency Control in Microgrids: An Adaptive Fuzzy-Neural-Network Virtual Synchronous Generator

• URL: http://arxiv.org/abs/2506.18611v1
• Date: Mon, 23 Jun 2025 13:16:52 GMT
• Title: Frequency Control in Microgrids: An Adaptive Fuzzy-Neural-Network Virtual Synchronous Generator
• Authors: Waleed Breesam, Rezvan Alamian, Nima Tashakor, Brahim Elkhalil Youcefa, Stefan M. Goetz,
• Abstract summary: This paper proposes a method to adapt the inertia, damping, and droop parameters dynamically through a fuzzy neural network controller.<n>Compared to traditional and fuzzy logic controller methods, the results demonstrate that the proposed method significantly reduces the frequency deviation to less than 0.03 Hz and shortens the stabilizing/recovery time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reliance on distributed renewable energy has increased recently. As a result, power electronic-based distributed generators replaced synchronous generators which led to a change in the dynamic characteristics of the microgrid. Most critically, they reduced system inertia and damping. Virtual synchronous generators emulated in power electronics, which mimic the dynamic behaviour of synchronous generators, are meant to fix this problem. However, fixed virtual synchronous generator parameters cannot guarantee a frequency regulation within the acceptable tolerance range. Conversely, a dynamic adjustment of these virtual parameters promises robust solution with stable frequency. This paper proposes a method to adapt the inertia, damping, and droop parameters dynamically through a fuzzy neural network controller. This controller trains itself online to choose appropriate values for these virtual parameters. The proposed method can be applied to a typical AC microgrid by considering the penetration and impact of renewable energy sources. We study the system in a MATLAB/Simulink model and validate it experimentally in real time using hardware-in-the-loop based on an embedded ARM system (SAM3X8E, Cortex-M3). Compared to traditional and fuzzy logic controller methods, the results demonstrate that the proposed method significantly reduces the frequency deviation to less than 0.03 Hz and shortens the stabilizing/recovery time.

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