基于EWM-TOPSIS的退役风电机组改造方案评估

ISSN:2705-0998(P)

EISSN:2705-0513(O)

语言:中文

作者
文雅靖,于 雪
文章摘要
合理处理退役风电机组,能够降低企业成本,为环境保护和资源节约做出贡献。本文构造风电机组退役改造方案选择指标体系,然后基于EWM-TOPSIS法进行方案选择,最后通过数值分析验证模型方法的有效性。结果证明,本文所图提研究方法对风电机组的退役改造方案可以进行有效的评价研究。
文章关键词
EWM;TOPSIS;风电机组;退役改造
参考文献
[1] Odin Foldvik Eikeland, Finn Dag Hovem, Tom Eirik Olsen, Matteo Chiesa, Filippo Maria Bianchi,Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case, [J],Energy Conversion and Management: X, 2022, Volume 15,ISSN 2590-1745.[2] Pejman Bahramian, Glenn P. Jenkins, Frank Milne,The displacement impacts of wind power electricity generation: Costly lessons from Ontario,[J].Energy Policy,2021,Volume 152,ISSN 0301-4215. [3] Priyanka Malhan, Monika Mittal,A novel ensemble model for long-term forecasting of wind and hydro power generation,[J].Energy Conversion and Management,2022,Volume 251,ISSN 0196-8904. [4] Enrico G.A. Antonini, Tyler H. Ruggles, David J. Farnham, Ken Caldeira, The quantity-quality transition in the value of expanding wind and solar power generation,[J].iScience, 2022,Volume 25, Issue 4, ISSN 2589-0042. [5] 叶伟.退役潮将至风电叶片何去何从[N].中国高新技术产业导报,2022-04-04(013). [6] 金晓航,泮恒拓,徐正国.数据驱动的风电机组变桨系统状态监测[J].太阳能学报,2022,43(04):409-417. [7] 刘家瑞,杨国田,王孝伟.基于孪生深度神经网络的风电机组故障诊断方法[J/OL].系统仿真学报:1-11[2022-07-01]. [8] 李锁,黄玲玲,刘阳,苗育植.基于风电机组状态信息的海上风电场维护策略[J].现代电力,2022,39(01):26-35. [9] 田锰,吴劲芳,杨林,贾洪岩,魏宏杰,朱董军.风电场运维管理体系实践[J].电力安全技术,2020,22(07):25-28.
Full Text:
DOI