A novel power curve modeling framework for wind turbines

Abstract

This paper presents two main novelties concerning power curve modeling of wind turbines. First novelty lies in the hybridization of 5 widely-used parametric functions and 8 recently-developed metaheuristic optimization algorithms. While constructing new hybrid power curve models, design coefficients of 4-parameter and 5-parameter logistic, 5th-order and 6th-order polynomial and modified hyperbolic tangent functions are fitted with ant lion, grey wolf, moth-flame and multi-verse optimizers and whale optimization, sine cosine, salp swarm and dragonfly algorithms. The best hybrid power curve model is achieved by the grey wolf optimizer-based modified hyperbolic tangent function in terms of the goodness-of-fit indicators. Second novelty lies in the integration of a well-known partitional clustering method to the best hybrid power curve model developed. While building a novel integrative power curve model, design coefficients of grey wolf optimizer-based modified hyperbolic tangent function are solved using only the highly representative data points identified by the Squared Euclidean-based k-means clustering algorithm. The operational characteristics of the wind turbine power curve are reflected with a higher accuracy. As a crucial result, the proposed power curve modeling framework is shown to be superior for wind turbines.