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
  • [41] Collaborative Wind Power Forecast
    Almeida, Vania
    Gama, Joao
    ADAPTIVE AND INTELLIGENT SYSTEMS, ICAIS 2014, 2014, 8779 : 162 - 171
  • [42] Wind Velocity - Power Curve of Wind Turbine
    Kacor, Petr
    PROCEEDINGS OF THE 13TH INTERNATIONAL SCIENTIFIC CONFERENCE ELECTRIC POWER ENGINEERING 2012, VOLS 1 AND 2, 2012, : 633 - 637
  • [43] Modeling and Simulation of a Hybrid Power Generation System of Wind turbine, Micro-turbine and Solar Heater Cells
    Abdel-Geliel, Mostafa
    Zidane, Iham F.
    Anany, Mohammed
    Rezeka, Sohair F.
    11TH IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA), 2014, : 1304 - 1309
  • [44] Wind Turbine Power Curve Design for Optimal Power Generation in Wind Farms Considering Wake Effect
    Tian, Jie
    Zhou, Dao
    Su, Chi
    Soltani, Mohsen
    Chen, Zhe
    Blaabjerg, Frede
    ENERGIES, 2017, 10 (03):
  • [45] A Multiscale Wind and Power Forecast System for Wind Farms
    Rasheed, Adil
    Suld, Jakob Kristoffer
    Kvamsdal, Trond
    EERA DEEPWIND' 2014, 11TH DEEP SEA OFFSHORE WIND R&D CONFERENCE, 2014, 53 : 290 - 299
  • [46] Hybrid ANFIS-PI-Based Robust Control of Wind Turbine Power Generation System
    Ishaque, Muhammad
    Laghari, Javed Ahmed
    Bhayo, Muhammad Akram
    Chandio, Sadullah
    Mahariq, Ibrahim
    INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2024, 2024
  • [47] Generation Scheduling for Wind Power Generation by Storage Battery System and Meteorological Forecast
    Tanabe, T.
    Sato, T.
    Tanikawa, R.
    Aoki, I.
    Funabashi, T.
    Yokoyama, R.
    2008 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, VOLS 1-11, 2008, : 5477 - +
  • [48] Increasing Wind Power Penetration Level Based on Hybrid Wind and Photovoltaic Generation
    Liu, Jun
    Fang, Wanliang
    Yang, Yongqian
    Yang, Chunxiang
    Lei, Shen
    Fu, Suilin
    2013 IEEE INTERNATIONAL CONFERENCE OF IEEE REGION 10 (TENCON), 2013,
  • [49] Novel Power Smoothing and Generation Scheduling Strategies for a Hybrid Wind and Marine Current Turbine System
    Anwar, Muhammad Bashar
    El Moursi, Mohamed Shawky
    Xiao, Weidong
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2017, 32 (02) : 1315 - 1326
  • [50] Transient Analysis of Power Generation Systems using Wind Turbine-Hybrid on Bakaru System
    Asri, Andarini
    Marwan
    Lukman, Musfirah Putri
    Naim, Kurniawati
    Bachtiar, Muh. Imran
    Utomo, Bayu Tri
    2021 7TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND INFORMATION ENGINEERING (ICEEIE 2021), 2021, : 21 - 26