인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
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.
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.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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