Advanced modeling and control of 5 MW wind turbine using global optimization algorithms

Abstract

This article presents a methodological approach for controller gain tuning of wind turbines using global optimization algorithms. For this purpose, the wind turbine structural and aerodynamic modeling are first described and a complete model for a 5 MW wind turbine is developed as a case study based on a systematic modeling approach. The turbine control requirements are then described and classified using its power curve to generate an appropriate control structure for satisfying all turbine control modes simultaneously. Next, the controller gain tuning procedure is formulated as an engineering optimization problem where the command tracking error and minimum response time are defined as objective function indices and physical limitations (overspeed and oscillatory response) are considered as penalty functions. Taking the nonlinear nature of the turbine model and its controller into account, two meta-heuristic global optimization algorithms (Imperialist Competitive Algorithm and Differential Evolution) are used to deal with the defined objective functions where the mechanism of interaction between the defined problem and the used algorithms are presented in a flowchart feature. The results confirm that the proposed approach is satisfactory and both algorithms are able to achieve the optimized controller for the wind turbine.

Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

“…Table 1 shows the properties for the NREL 5-MW Baseline Wind Turbine. More information on the parameters of the analyzed turbine can be found in [58][59][60]. The proposed design is based on linear models.…”

Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data

“…The simplest method to apply the model may be to define a number of intervals for the wind speed, to identify each interval by the mean value and to assign the mean output power as the corresponding value for each wind speed identifier. With these pairs of values, i.e., points in a graph, the model can be defined as the function that best represents the points, by using an optimization process for an objective function which is the error made [29][30][31]. An optimization process is based on an algorithm and, in this paper, the Interior Point Algorithm has been chosen because it is well known due to its satisfactory performance when dealing with a wide variety of problems.…”