基于FAHP与SSA-BP神经网络的海洋发电项目风险分析  

Risk Analysis of Marine Power Generation Projects Based on FAHP and SSA-BP Neural Network

作　　者：李一凡 刘晓捷 LI Yifan;LIU Xiaojie(School of Civil Engineering,Hebei University of Engineering,Handan 056038,Hebei China)

机构地区：[1]河北工程大学土木工程学院,河北邯郸056038

出　　处：《河南科学》2023年第9期1257-1265,共9页Henan Science

摘　　要：为有效解决风力、光伏联合发电的随机性、波动性和间歇性带来的风险问题,实现海洋风-光联合发电项目的高效建造及后期的稳定运行,从自然、政策、经济、设计、建设、运维6个方面进行风险识别,结合专家对各个风险因素的打分,运用FAHP与麻雀搜索算法改进BP神经网络结合的方法对项目存在的风险进行评价,得到主要风险为政府扶持补贴政策改变风险、施工安全风险、弃风限电风险;其他为中级风险与较小风险,并对主要风险提出应对措施.结果验证了基于FAHP与SSA-BP神经网络在风险评估工作中的客观性、稳定性与适用性.To effectively address the risks associated with the randomness,volatility and intermittency of wind and photovoltaic joint power generation,and to achieve efficient construction and stable operation of marine wind-photovoltaic joint power generation projects,risk identification is conducted from six aspects of nature,policy,economy,design,construction,operation and maintenance.Combined with the expert scoring of various risk factors,the methods of FAHP and BP neural network improve by sparrow search algorithm are used to evaluate the risks existing in the project.The results show that the main risks are the change risks of government support and subsidy policies,construction safety risks and wind power curtailment risks.Others are intermediate risks or minor risks.Countermeasures have been proposed to address these main risks.The objectivity,stability and applicability of the FAHP and SSA-BP neural networks in risk assessment have been validated.

关 键 词：海洋风-光联合发电 模糊层次分析法 麻雀搜索算法 BP神经网络 风险识别 风险评价 风险应对 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TM614[自动化与计算机技术—计算机科学与技术] TM615[电气工程—电力系统及自动化]