Adaptive control of the virtual synchronous generator by deep neural networks for a wind high power conversion chain
深層ニューラルネットワークによる仮想同期発電機の適応制御：風力高電力変換チェーン向け (AI 翻訳)
Maataoui, Wijdane El, Abounada, Abdelouahed
🤖 gxceed AI 要約
日本語
本論文は、風力発電システムにおいて仮想同期発電機(VSG)の制御を深層ニューラルネットワーク(DNN)で完全に置き換える手法を提案。従来のVSG制御と比較して、周波数・電圧安定性の向上、有効電力追従の改善、および高調波歪率(THD)を0.04%に低減（従来0.51%）する成果を得た。再エネ系统の知的制御へのAI応用の可能性を示す。
English
This paper proposes replacing the classical virtual synchronous generator (VSG) control with a deep neural network (DNN) for wind power systems. The DNN is trained end-to-end using supervised learning to generate inverter control signals from electrical measurements. Simulation results show improved frequency and voltage stability, better active power tracking, and a reduction in total harmonic distortion (THD) from 0.51% to 0.04%. The work demonstrates the potential of AI-based intelligent control for renewable energy integration.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、再エネ大量導入時の系統安定化が重要課題であり、本件のDNNベースVSG制御は系統慣性確保や電力品質向上に寄与する可能性がある。特に、変動の大きい風力発電への適用が期待される。
In the global GX context
As renewable energy penetration increases globally, maintaining grid stability with virtual synchronous generators is crucial. This DNN-based control approach offers a flexible, high-performance alternative to conventional VSG control, potentially improving power quality and enabling higher renewable shares. The significant THD reduction (0.04%) is particularly noteworthy for grid code compliance.
👥 読者別の含意
🔬研究者:This paper presents a novel end-to-end DNN control for VSG, offering a new direction for AI-based power electronics control in renewable energy systems.
🏢実務担当者:Wind farm operators and inverter manufacturers can consider adopting DNN-based VSG control to enhance power quality and grid stability, reducing harmonic distortion.
🏛政策担当者:Regulators could note that AI-driven control can facilitate higher renewable energy integration without compromising grid stability, potentially informing grid code updates.
📄 Abstract（原文）
The virtual synchronous generator (VSG) is commonly used to reproduce the inertial response of conventional synchronous machines. However, the VSG control architecture relies on controller chains, benchmark transformations, and parameter settings, including virtual inertia and damping, which limit its flexibility in highly dynamic environments. This paper proposes an innovative end-to-end control approach based on a neural network to fully replace the classical VSG control structure. The neural network developed is trained to directly generate inverter control signals from real-time electrical measurements, including voltages and currents, as well as active and reactive power. A dataset is generated from a detailed VSG model under different operating conditions, and then a multilayer neural network is trained using supervised learning with MATLAB. The resulting model is then integrated into a complete wind energy conversion chain simulated in Simulink. The simulation results demonstrate that control based on artificial neural networks ensures better frequency and voltage stability, more accurate tracking of the active power injected, and a significant improvement in power quality, with total harmonic distortion (THD) reduced to 0.04%, compared to 0.51% for conventional VSG control. These results confirm the potential of artificial intelligence-based approaches for the intelligent control of renewable energy systems.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20638426first seen 2026-06-12 04:19:49
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