Neuro-Fuzzy Adaptive Model Predictive Control for Enhanced Voltage Stability in Transmission Systems
送電系統における電圧安定性向上のためのニューロファジィ適応モデル予測制御 (AI 翻訳)
Mekuriaw M
🤖 gxceed AI 要約
日本語
本論文は、ニューロファジィ適応モデル予測制御（ANFIS-MPC）を提案し、送電系統の電圧安定性を向上させる。再生可能エネルギーの導入による擾乱下で、従来のMPCよりも68.7%速い電圧回復と0.91p.u.の電圧最低点を達成した。エチオピアの400kV/230kV系統での検証により、AI駆動の適応制御が長距離送電の安定性に有効であることを示した。
English
This paper proposes a Neuro-Fuzzy Adaptive Model Predictive Control (ANFIS-MPC) to enhance voltage stability in transmission systems. Under disturbances from renewable integration, it achieves 68.7% faster voltage recovery and a voltage nadir of 0.91 p.u. compared to conventional MPC. Validation on the Ethiopian 400kV/230kV network demonstrates the effectiveness of AI-driven adaptive control for long-distance transmission stability.
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
日本の電力系統でも再生可能エネルギーの大量導入に伴い電圧安定性が課題となっている。本論文の適応制御手法は、将来の系統運用に示唆を与える可能性があるが、日本の系統構成とは異なる点に留意が必要。
In the global GX context
As renewable energy integration increases globally, grid stability challenges become critical. This adaptive control approach could inform voltage regulation strategies for transmission system operators worldwide, especially in developing power infrastructures.
👥 読者別の含意
🔬研究者:Provides a novel dual-matrix adaptation algorithm for voltage stability control with potential application to renewable-rich grids.
🏢実務担当者:Offers a real-time adaptive control method that could improve voltage restoration performance in transmission networks.
📄 Abstract（原文）
<title>Abstract</title> <p>Contemporary high-voltage (HV) transmission networks are increasingly strained by rapid load growth and the stochastic integration of renewable energy resources, forcing grids to operate perilously close to their critical stability margins. Voltage collapse—the progressive, irreversible decline of bus voltages culminating in widespread blackouts—represents the most severe consequence of this operational stress. While Model Predictive Control (MPC) offers systematic, constraint-aware trajectory optimisation for voltage regulation, conventional implementations rely on static weighting matrices that become suboptimal during severe disturbances. Here we present a novel Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPC strategy that simultaneously co-adapts both the state penalty matrix Q and the control effort penalty matrix R in real time—a dual-matrix adaptation capability not simultaneously provided by prior ANFIS-MPC approaches. A Sugeno-type ANFIS trained on 12 000 simulation samples (covering N-1, N-2, and renewable fluctuation scenarios) uses real-time voltage error and its rate of change to drive the adaptation. Validated on a high-fidelity model of the Ethiopian 400 kV and 230 kV transmission network under critical N-2 contingency conditions, the ANFIS-MPC achieves a 68.7 % improvement in voltage restoration speed (1.5 s versus 4.8 s for conventional MPC) and elevates the voltage nadir from 0.81 p.u. to 0.91 p.u., keeping the system above the critical V-Q nose-point. These findings establish that AI-driven dual-matrix adaptation is an essential advancement for securing long-distance transmission corridors in developing power infrastructures.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-9758864/v1first seen 2026-06-04 04:24:23 · last seen 2026-06-16 04:30:26
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