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Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees.

Khurram Mushtaq1, Runmin Zou1, Asim Waris2

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Summary
This summary is machine-generated.

This study enhances wind turbine power curve (WTPC) modeling by using multivariate data and MARS techniques. These methods improve accuracy and effectively handle data outliers for better wind power forecasting and condition monitoring.

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Area of Science:

  • Renewable Energy Engineering
  • Data Science
  • Machine Learning

Background:

  • Wind turbine power curve (WTPC) is crucial for monitoring and forecasting.
  • Existing WTPC models struggle with complex environmental factors, technical issues, and data outliers, limiting accuracy.
  • Pre-processing techniques are insufficient to address inherent data inconsistencies.

Purpose of the Study:

  • To improve the accuracy of wind turbine power curve (WTPC) models.
  • To address limitations in modeling complex non-linear relationships and handling data outliers.
  • To develop a robust WTPC modeling technique for enhanced wind energy assessment.

Main Methods:

  • Development of multivariate WTPC models incorporating additional input variables.
  • Application of Multivariate Adaptive Regression Splines (MARS) for flexible non-linear modeling.
  • Implementation of a novel outlier detection method based on error distribution analysis.

Main Results:

  • Multivariate models significantly improved power curve estimation accuracy compared to univariate models.
  • MARS demonstrated superior non-linear fitting capabilities, outperforming regression trees and other methods.
  • The proposed methods effectively mitigated the adverse effects of hidden outliers, leading to more converged error distributions.

Conclusions:

  • Multivariate modeling and MARS are effective techniques for enhancing WTPC accuracy.
  • The developed outlier detection method successfully identifies and addresses hidden outliers in WTPC data.
  • This research provides a more reliable approach for wind turbine condition monitoring and power forecasting.