| Title |
AI-Based Internal Power Grid Topology Change System for Optimal Operation of Offshore Wind Farms |
| Authors |
김민재(Min-Jae Kim) ; 방준호(Junho Bang) ; 김든찬(Deunchan Kim) ; 김지원(Ji-Won Kim) ; 박소연(Soyeon Park) ; 강해권(Hae-Gweon Kang) ; 권명회(Myeong-Hoi Kwon) |
| DOI |
https://doi.org/10.5370/KIEE.2024.73.11.2045 |
| Keywords |
Topology; Reinforce learning; DQN(Deep Q-Learning); Transmission Line Reconfiguration; Energy Loss Optimization |
| Abstract |
Offshore wind power is gaining attraction as a sustainable energy solution, but optimizing topologies for changing environments remains a significant challenge. Existing algorithms design static topologies based on specific environmental conditions, which limits the flexibility of real-time adaptation.
In this study, we propose a dynamic topology optimization technique using deep Q-networks (DQN) to address this problem. We model offshore wind farm topology optimization as a Markov decision process (MDP) and apply DQNs to solve it in real-time.
Experiments are conducted through simulations using an offshore wind farm model with 40 wind turbines (5MW). DQN-based optimization achieved an annual energy production of 894.7 GWh and an average transmission loss rate of 4.80%, outperforming the fixed topology and random breaker switching methods. DQN showed high adaptability to seasonal wind direction changes and power demand fluctuations, maintaining stable performance throughout the year. |