In this study, the AHP (analytic hierarchy process) technique was used to analyze the risk of expected risk factors and fishing possibilities during gillnet fishing within the floating offshore wind farms (floating OWF). For this purpose, the risks that may occur during gillnet fishing within the floating offshore wind farms were defined as collisions, entanglements, and snags. In addition, the risk factors that cause these risks were classified into three upper risk factors and ten sub risk factors, and the three alternatives to gillnet fishing available within the floating OWF were classified and a hierarchy was established. Lastly, a survey was conducted targeting fisheries and marine experts and the response results were analyzed. As a result of the analysis, among the top risk factors, the risk was the greatest when laying fishing gear. The risk of the sub factors for each upper risk was found to be the highest at the berthing (mooring), the final hauling of fishing net, and the laying of the bottom layer net. Based on the alternatives, the average of the integrated risk rankings showed that allowing full navigation/fisheries had the highest risk. As a result of the final ranking analysis of the integrated risk, the overall ranking of allowing navigation/fisheries in areas where bottom layer nets were laid was ranked the first when moving vessels within the floating OWF was analyzed as the lowest integrated risk ranking of the 30th at the ban on navigation/fisheries. Through this, navigation was analyzed to be possible while it was analyzed that the possibility of gillnet fishing within the floating OWF was not high.

신재생 에너지 자원중 풍력발전은 비약적인 기술 발전과 시장 규모가 급속하게 성장하고 있다. 최근 육상풍력발전단지의 공간적 한계, 환경 문제 등으로 인하여 설치 공간이 해상으로 이동되었고, 더욱 풍부한 풍황 조건을 가진 깊은 수심에 설치되는 부유식 해상 풍력단지의 개발이 활발하게 진행되고 있다. 해상교통관점에서 해상풍력단지의 최적위치 선정은 선박과 풍력기들의 간섭을 최소화 하고 사고 확률이 적은 곳이며, 선박 밀집도가 낮은 해역이 최적위치로 선정된다. 본 연구에서는 유전 알고리즘 기반의 계절별 1주일 기간 선박자동식별장치 데이터를 유전자 및 염색체로 구성하였다. 80개의 유전자로 구성하고 유전 알고리즘의 적합도 평가를 거쳐 부유식 해상 풍력단지의 계절별 최적위치를 선정하였다. 더 나아가 계절별 최적위치 점수를 합산하여 최종 최적위치를 선정하였다. 분석 해역에서 최적위치는 11개로 나타났으며, 해상교통관점에서 유전 알고리즘을 통한 최적위치 선정이 적용 가능함을 확인하였다.