Original Research Article Coordinated controller design of grid connected DFIG based wind turbine using response surface methodology and NSGA II Sharon Ravichandran a,⇑ , R.P. Kumudinidevi a , S.G. Bharathidasan b , V. Evangelin Jeba a a College of Engineering-Guindy, Anna University, Chennai 600 025, India b Sri Venkateshwara College of Engineering, Chennai 602 105, India article info Article history: Received 10 December 2013 Revised 26 June 2014 Accepted 8 August 2014 Keywords: Doubly fed induction generator Response surface methodology Nondominated sorting genetic algorithm II Maximum power point tracking (MPPT) abstract This paper presents a novel design procedure for the coordinated tuning of rotor side converter (RSC) and grid side converter (GSC) controllers of doubly fed induction generator (DFIG) wind turbine system. The RSC and GSC controller parameters are determined by simultaneously optimizing the controller perfor- mance indices. The performance indices considered are maximum peak overshoot (MPOS x ), settling time (Tss x ) of the generator speed and the maximum peak overshoot (MPOS Vdc ), maximum peak undershoot (MPUS Vdc ) and settling time (Tss Vdc ) of DC link voltage. The coordinated controller design is carried out in two steps. First step is to arrive at the analytical expression that relates the performance indices and the controller parameters. This is achieved using response surface methodology (RSM) thereby saving signif- icant computational time. In the second step the determination of controller parameters is posed as a constrained multiobjective optimization problem. The constrained multiobjective optimization problem is solved using NSGAII (nondominated sorting genetic algorithm II). The proposed methodology is tested on a sample system with DFIG based WECS. Simulation results demonstrate the effectiveness of the pro- posed methodology. Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction The rapid depletion of fossil fuels compounded by their detri- mental environmental effects has accelerated the growth of wind energy. Currently, wind energy is one of the key players in the elec- tric power sector [1]. The advent of variable speed WECS enabled overcoming one of the major impediments to the expansion of wind, its lack of controllability and flexibility. Variable speed WECS with their better energy capture, smoother operation, lower flicker and superior controllability [2–5] have superseded the fixed speed WECS and play a major role in expediting the growth of wind energy. The most commonly installed variable speed WECS is based on DFIG and it constitutes around 55% of the total market share. Wind speed is variable in nature. Therefore, in order to maxi- mize the power extracted from wind (speeds between the cut in and cut off speed) WTs operate continuously at the optimum tip speed ratio by changing the generator speed in proportion to the wind speed.This is achieved by RSC. GSC controls the power flow between the DC-link and the grid to maintain the DC-link capacitor voltage at a constant value. Hence performance of a DFIG is largely dependent on its converter and associated controls. The most pop- ular and practical control scheme of DFIG is vector-oriented control based on proportional-integral (PI) controller [6–11]. Due to the stochastic nature of wind, generator speed varies continuously to track the maximum power point. These speed vari- ations are translated into generator output power variations and in turn into DC link voltage fluctuations. Hence there arises a need for coordinated tuning of RSC and GSC controllers. Therefore optimiza- tion techniques are being utilized for the coordinated tuning of controllers. However, coordinated tuning of controllers using opti- mization techniques becomes cumbersome, challenging and com- putationally inefficient with increase in the number of the controller parameters [11–13]. This paper proposes a simplistic and computationally efficient design procedure for coordinated tuning of RSC and GSC control- lers of DFIG based wind turbine system. In this paper Response sur- face methodology (RSM) is used to formulate the analytical expression that relates the responses and controller parameters thereby saving significant computational time. RSM is a collection of mathematical and statistical techniques that are useful for modeling and analysis of a system whose response is influenced by several variables and the objective is to optimize the response [14–18]. This paper applies RSM for the coordinated tuning of http://dx.doi.org/10.1016/j.seta.2014.08.004 2213-1388/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +91 9715273737. E-mail addresses: sharonravi87@gmail.com (S. Ravichandran), bharathi_99@ yahoo.com (S.G. Bharathidasan). Sustainable Energy Technologies and Assessments 8 (2014) 120–130 Contents lists available at ScienceDirect Sustainable Energy Technologies and Assessments journal homepage: www.elsevier.com/locate/seta