Machine learning–based piecewise affine model of wind turbines during maximum power point tracking

Abstract

In this paper, a discrete-time piecewise affine (PWA) model of a wind turbine during Maximum Power Point Tracking (MPPT) region is identified. A clustering-based identification method is utilized to create PWA maps for nonlinear aerodynamic torque and thrust force functions. This method exploits the combined use of clustering, pattern recognition, and parameter identification techniques. The well-known K-means clustering method is employed along with a perceptron-based multiclassifier for pattern recognition and the least squared technique for parameter estimation. The identified maps are approximated the nonlinear static functions of the dynamic model of the wind turbine. Characteristics of a 5-MW wind turbine are considered and the resulting model, which consists of 25 subregions is compared with the nonlinear dynamic model. Two test cases are studied in order to validate the presented model. Simulation results demonstrate the effectiveness and accuracy of the PWA model such that the response of the identified PWA model is fitted well to the nonlinear one. The PWA model identified in this paper can be widely used for advanced control systems design and long-term performance and security assessment of the power grid.

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

“…To optimize objective function J 2 , it is critical to reasonably divide the input space Ω N into S subspaces that satisfy conditional Equation (25), so as to determine the number of local models:…”

“…Muhammad and Michael [24] applied a PWA modeling method to optimal planning of thermal energy systems for microgrids. Sindareh-Esfahani [25] identified a discrete-time PWA model of a wind turbine during Maximum Power Point Tracking region. Mattsson and Zachariah [26] applied the PWA modeling method to the identification of discrete-time nonlinear dynamic models of cascade tanks.…”

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“…Due to its great approximation capability, the PWA (Piecewise Affine) model is a crucial data-driven technique that has attracted a lot of attention and is already being utilized to solve difficult issues in simulation, prediction, and system analysis [14]. Typical application fields of PWA models are providing solutions for vehicle powertrains [14-17], robotics [18], power and energy [19], industry production processes [20][21][22], and rainfall runoff [23]. Also, the PWA modeling method was adopted for motion segmentation in computer vision [24,25].…”

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“…In [16]- [18], and [19], the authors use the neuro-fuzzy approximations for the perturbed plants regulation. The adaptive laws for the perturbed plants regulation are focused in [20], [21], and [22]. In [23] and [24], the structure theory for the perturbations attenuation is mentioned.…”