Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

被引:16
|
作者
Adedeji, Paul A. [1 ]
Akinlabi, Stephen A. [2 ]
Madushele, Nkosinathi [1 ]
Olatunji, Obafemi O. [1 ]
机构
[1] Univ Johannesburg, Dept Mech Engn Sci, Johannesburg, South Africa
[2] Walter Sisulu Univ, Dept Mech Engn, Mthatha, South Africa
关键词
ANFIS; embedded generation; genetic algorithm; particle swarm optimization; single facility location; South Africa; utility-scale wind turbine; wind energy; FACILITY LOCATION; PERFORMANCE EVALUATION; GENETIC ALGORITHM; FUZZY; ALLOCATION; SYSTEM; ANFIS; PSO; GA; FARMS;
D O I
10.1002/er.5620
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Wind energy uptake in South Africa is significantly increasing both at the micro- and macro-level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility-scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short-term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short-term power output. The feasibility of embedded generation for typical medium-scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG-1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO-ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria wasX= 19.24 m,Y= 47.11 m. This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers.
引用
收藏
页码:413 / 428
页数:16
相关论文
共 50 条
  • [31] Wind power plants hybridised with solar power: A generation forecast perspective
    Couto, Antonio
    Estanqueiro, Ana
    JOURNAL OF CLEANER PRODUCTION, 2023, 423
  • [32] THE DARRIEUS WIND TURBINE FOR ELECTRICAL-POWER GENERATION
    ROBINSON, ML
    AERONAUTICAL JOURNAL, 1981, 85 (845): : 244 - 255
  • [33] Vertical Axis Wind Turbine Application for Power Generation
    Hussein, Dildar
    Ahmad, Khairol Amali
    Nizan, Ahmad Zulhelmi Mat
    Makmor, Nazrul Fariq
    Hashim, Fakroul Ridzuan
    JURNAL KEJURUTERAAN, 2021, 4 (02): : 23 - 27
  • [34] Enhancing Wind Turbine Power Forecast via Convolutional Neural Network
    Liu, Tianyang
    Huang, Zunkai
    Tian, Li
    Zhu, Yongxin
    Wang, Hui
    Feng, Songlin
    ELECTRONICS, 2021, 10 (03) : 1 - 13
  • [35] Location and turbine parameter selection for offshore wind power maximization
    Tusar, Md Imran Hasan
    Sarker, Bhaba R.
    WIND ENGINEERING, 2023, 47 (04) : 833 - 851
  • [36] Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation
    Wang, Jingxing
    AlShelahi, Abdullah
    You, Mingdi
    Byon, Eunshin
    Saigal, Romesh
    IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2021, 12 (04) : 1864 - 1875
  • [37] Modeling Conditional Forecast Error for Wind Power in Generation Scheduling
    Zhang, Ning
    Kang, Chongqing
    Xia, Qing
    Liang, Ji
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2014, 29 (03) : 1316 - 1324
  • [39] Wavelet packet diagnosis for wind turbine and vector control for wind power generation system
    Hou, Zhaoran
    APPLIED ENERGY TECHNOLOGY, PTS 1 AND 2, 2013, 724-725 : 641 - 644
  • [40] Wind Turbine Reactive Power Control in a Multiterminal DC (MTDC) Wind Generation System
    Gholamian, Mahzad
    Beik, Omid
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2025, 61 (01) : 666 - 675