Multi-gen genetic programming based improved innovative model for extrapolation of wind data at high altitudes, case study: Turkey

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@Article{EMEKSIZ:2022:compeleceng,

• author = "Cem Emeksiz",
• title = "Multi-gen genetic programming based improved innovative model for extrapolation of wind data at high altitudes, case study: Turkey",
• journal = "Computers and Electrical Engineering",
• volume = "100",
• pages = "107966",
• year = "2022",
• ISSN = "0045-7906",
• DOI = "doi:10.1016/j.compeleceng.2022.107966",
• URL = "https://www.sciencedirect.com/science/article/pii/S0045790622002427",
• keywords = "genetic algorithms, genetic programming, Log-law, Multi-gen genetic programming, Power-law, Wind shear coefficient, Wind speed extrapolation",
• abstract = "Wind speed is the most important input of wind energy conversion systems and has higher values at high altitudes. Therefore, tall wind measurement masts are used in the wind power industry to determine the wind speed at high altitudes. However, this situation brings many engineering problems (cost escalation, de-erection and re-erection of the masts due to the failure of the anemometer and sensors, lightning strikes, mechanical failures etc.). In this study, it is aimed to estimate the data at the hub height levels of the proposed wind power generators by placing shorter wind masts as a suitable alternative for longer masts. Therefore, we proposed an innovative model that uses multigene genetic programming to estimate wind speed at high altitudes. According to the power and logarithmic law, analysis results show that root mean square error (RMSE) values were decreased with proposed method in the wind speed estimation, 58.62percent and 58.77percent respectively",

}

Genetic Programming entries for Cem Emeksiz

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