Study on a Development Model of Wind Power Generation Integrating an Artificial Neural Network

Kyoung-Soo Kim; Ilsik Jang

doi:10.32390/ksmer.2023.60.4.231

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2023 Vol.60, Issue 4 Preview Page Next Page

Research Paper

Study on a Development Model of Wind Power Generation Integrating an Artificial Neural Network 인공신경망을 통합한 풍력발전 개발모델 연구

31 August 2023. pp. 231-241

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Abstract

Despite the Korean government’s keen interest in wind power generation, wind farm construction continues to be ad hoc in terms of both selection of location and utilization wind resources in the country. In this study, an artificial neural network (ANN) model was developed to predict the economics of wind power generation using wind vector characteristics, grid linkage, and geographical characteristics. A regional wind power resource distribution map was constructed considering economic feasibility. An optimization method was established to optimize the size of the wind power complex, grid connection, and type of wind turbine to secure maximum economic benefits for promising wind power locations. The optimization method used could quantify various key factors and support economic analysis through a sensitivity analysis between the various impact factors of wind power projects. The optimization method led to identifying locations for onshore and offshore wind farms with high economic potential in the Southwest Sea and near Jeju Island.

Keywords

wind power generation

artificial neural network

economic analysis

resource distribution map

optimization method

우리나라의 풍력발전은 정부와 지자체의 높은 관심에도 불구하고 입지선정과 풍력자원의 활용에서 임기응변식 풍력단지조성 및 실증단계에 머물러 있다. 이 연구는 국내 풍력의 자원화에 영향을 미치는 인자인 바람의 벡터적 특성, 계통연계, 지리적 특성 등을 사용하여 풍력발전의 경제성을 예측하는 인공신경망 모델을 개발하였으며, 이를 통해 경제성 측면에서 광역적 풍력자원 분포도를 구축하였다. 분포도에서 확인한 풍력발전 유망위치에 대해 최대의 경제성을 확보하기 위한 단지규모, 계통연계 방안, 풍력터빈의 종류를 최적화하는 최적화 기법을 구축하였다. 최적화 기법은 풍력사업 영향인자들 간의 민감도분석을 통해 각종 주요인자를 정량화하고 경제성 분석 결과를 도출할 수 있다. 최적화 기법을 통해 서남해 및 제주도 부근의 경제성이 높은 육상 및 해상 풍력개발 가능 단지를 도출하였다.

키워드

풍력발전

인공신경망

경제성분석

자원분포도

최적화 기법

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Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
Journal Title(Ko) :한국자원공학회지
Volume : 60
No :4
Pages :231-241
Received Date : 2023-07-17
Revised Date : 2023-08-14
Accepted Date : 2023-08-28
DOI :https://doi.org/10.32390/ksmer.2023.60.4.231

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

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