Journal of Renewable Energy

Volume 2015, Article ID 978216, 10 pages

http://dx.doi.org/10.1155/2015/978216

## Load Mitigation and Optimal Power Capture for Variable Speed Wind Turbine in Region 2

Department of Electrical Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore 575 025, India

Received 2 June 2015; Revised 20 August 2015; Accepted 7 September 2015

Academic Editor: Adnan Parlak

Copyright © 2015 Saravanakumar Rajendran and Debashisha Jena. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper proposes the two nonlinear controllers for variable speed wind turbine (VSWT) operating at below rated wind speed. The objective of the controller is to maximize the energy capture from the wind with reduced oscillation on the drive train. The conventional controllers such as aerodynamic torque feedforward (ATF) and indirect speed control (ISC) are adapted initially, which introduce more power loss, and the dynamic aspects of WT are not considered. In order to overcome the above drawbacks, modified nonlinear static state with feedback estimator (MNSSFE) and terminal sliding mode controller (TSMC) based on Modified Newton Raphson (MNR) wind speed estimator are proposed. The proposed controllers are simulated with nonlinear FAST (fatigue, aerodynamics, structures, and turbulence) WT dynamic simulation for different mean wind speeds at below rated wind speed. The frequency analysis of the drive train torque is done by taking the power spectral density (PSD) of low speed shaft torque. From the result, it is found that a trade-off is to be maintained between the transient load on the drive train and maximum power capture.

#### 1. Introduction

In recent years, wind energy is one of the major renewable energy sources because of environmental, social, and economic benefits. The major classifications of wind turbines (WT) are fixed speed wind turbine (FSWT) and VSWT. Compared with FSWT, VSWT has many advantages such as improved energy capture, reduction in transient load, and better power conditioning [1]. For any kind of WT, control strategies play a major role on WT characteristics and transient load to the network [2]. In VSWT, the operating regions are classified into two major categories, that is, below and above rated wind speed. At below rated wind speed, the main objective of the controller (i.e., torque control) is to optimize the wind energy capture by avoiding the transients in the turbine components especially in the drive train. At above rated wind speed, the major objective of the controller (i.e., pitch control) is to maintain the rated power of the WT. In [3], the maximum power for VSWT is achieved by PI (proportional integral) controller, which is based on the fuzzy system. Error is taken as the input to the controller, that is, difference between the actual and optimal rotor speed, and the output of the controller is generator torque. Fuzzy logic systems (FLS) are used for tuning the PI controller gains for various wind speed. PI gains are optimized for different wind speed by particle swarm optimization (PSO). In [4], radial bias function neural network (RBFNN) and torque observer based control algorithm are used to control the WT for optimal energy capture. RBFNN is trained online by using MPSO (modified particle swarm optimization) training algorithm. In order to achieve the maximum power, the difference between the actual and optimal rotor speed is to be minimized. In [5], a new maximum adaptive algorithm for extraction of optimal power is proposed for small WT. Perturb and absorb scheme is adapted for different wind speed to obtain optimum relationship for regulating the maximum power point. In [6], two control strategies are developed for optimal power extraction with reduced mechanical stress. The first one is tracking controller with wind speed estimator which ensures the optimal angular speed of the rotor. In the second one, a robust power tracking is developed by nonhomogenous quasicontinuous high order sliding mode controller without considering wind velocity. Maximum power extraction from VSWT is achieved by a Takagi-Sugeno-Kang (TSK) fuzzy model which is based on data driven model [7]. In TSK model, a combination of fuzzy clustering method and genetic algorithm (GA) is used for portioning the input-output space and least square (LS) algorithm is used for parameter estimation. Nonlinear static and dynamic state feedback linearization control are addressed in [8, 9], where both the single and two-mass model are taken into consideration and the wind speed is estimated by Newton Rapshon (NR) method. To accommodate the parameter uncertainty and robustness, a higher order sliding mode controller is proposed in [10], which ensures the stability of the controller in both regions, that is, below and above rated speed. Feedback torque control is applied for mathematical model FSWT for maximum power extraction [11]. In order to achieve the maximum power point in the WT, FLC tuned by GA is discussed in [12]. The width of the membership function in FLC is adjusted by GA. In [13], sliding mode controller (SMC) and integral sliding mode controller (ISMC) are designed for all the regions of variable speed variable pitch wind turbine (VSVPWT) with FAST simulator.

The objective of this paper is to prove the efficacy of nonlinear controllers which considers the dynamic aspect of the wind and aero turbine, without the wind speed measurement. Finally, the objective is to track the reference rotor speed asymptotically. This paper is organized as follows. The objective of the work is discussed in Section 2. Section 3 discusses the modeling of the two-mass model. The conventional and proposed controllers are discussed in Sections 4 and 5. In Section 6, FAST model results are analyzed. Finally, a conclusion is drawn from the obtained results in Section 7, which shows the proposed method is having better performance compared to other existing controllers.

#### 2. Problem Formulation

Generally, WT is classified into two types, that is, fixed and variable speed WT. Variable speed WT has more advanced and flexible operation than fixed speed WT. Operating regions in variable speed WT are divided into three types. Figure 1 shows the various operating region in variable speed WT.