A23 2011 IEEE Student Conference on Research and Development (SCOReD) Improving Dynamic Response of Wind Turbine Driven DFIG with Novel Approach Mostafa Eidiani*, Natan Asghari shahdehi Hossein Zeynal Department of Electrical Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran *E-mail: eidiani@bojnourdiau.ac.ir Centre of Electrical Energy Systems (CEES), Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia E-mail: hzeynal@ieee.org Abstract— The frequency converter is the most sensitive part in the variable-speed wind turbine generator system equipped with a double-fed induction generator (DFIG). The frequency converter is normally controlled by a set of PI controllers. In order to improve the response of DFIG when subjected to system disturbances, the best way is to tune the PI controllers of the frequency converter. Due to the high complexity of the system, the tuning of these PI controllers is very difficult. In this paper an approach is offered to improve the response of DFIG when subjected to system disturbances using Hybrid Particle Swarm Optimization and Genetic Algorithm (PSO-GA). In this case, tuning all PI controllers’ parameters is considered. The results show that the proposed algorithm is well suited in terms of accuracy and quick response. at the stator terminals, but the rotor terminals are connected to the grid via a partial-load variable frequency AC/DC/AC converter (VFC) and a transformer. The VFC only needs to handle a fraction (25-30%) of the total power to achieve full control of the generator. Compared to the fixed-speed wind turbine with IG, the VSWT with DFIG can provide decoupled control of the active and reactive power of the generator, more efficient energy production, improved. The behaviour of the VFC and the associated WTGS relies on the performance of its control system. Using well designed controllers, it is possible to increase the chance of the WTGS to remain in service during grid disturbances. In the last decade, various modern control techniques such as adaptive control, variable structure control and intelligent control [6]-[8], have been intensively studied for controlling the nonlinear components in power systems. However, these control techniques have few real applications probably due to their complicated structures or the lack of confidence in their stability. Therefore, the conventional PI controllers, are still the most commonly used control techniques in power systems due to their simple structures, as can be seen in the control of the WTs equipped with DFIGs [9, 10]. Unfortunately, tuning the PI controllers is tedious and might be difficult to tune the PI gains properly due to the nonlinearity and high complexity of the system. Over the years, heuristic search-based algorithms such as genetic algorithms (GAs), tabu search algorithm and simulated annealing (SA) have been used for power system stabilizers (PSS) design [11]. However, when the parameters which have been optimized are highly correlated, the performance of these heuristic search algorithms degrades [12]. Recently, a new technique has been successfully used for single- and multiobjective nonlinear optimization, based on swarm intelligence called particle swarm optimization (PSO) [13]. The use of PSO for designing a single PID controller in the automatic voltage regulator (AVR) system of a conventional turbo generator has been reported in [14]. This design, however, is based on the step response and did not investigate the transient performance of the controller. In [15] an approach is presented to use the particle swarm optimization algorithm to design the optimal PI controllers for the rotor side converter of the DFIG. In [21] it is shown that the particle Keywords- Double-fed induction generator (DFIG); variablespeed Wind Turbine; Genetic Algorithm (GA); Particle Swarm Optimization (PSO) I. INTRODUCTION Wind turbines can either operate at fixed speed or variable speed. For a fixed speed wind turbine, the generator is directly connected to the electrical grid. For a variable speed wind turbine, the generator is controlled by power electronic equipment. There are several reasons for using variable-speed operation of wind turbines; among those are the possibilities of reducing stresses of the mechanical structure, acoustic noise reduction and the possibility of controlling active and reactive power [1]. Most of the major wind turbine manufactures are developing new larger wind turbines in the 3-5 MW range [2]. These large wind turbines are all based on variable-speed operation with pitch control using a directly driven synchronous generator (without gearbox) or a double-fed induction generator (DFIG). Fixed-speed induction generators with stall control are regarded as unfeasible [3] for these large wind turbines. Today, double-fed induction generators are commonly used by the wind turbine industry for larger wind turbines [4,5]. Compared to the variable speed wind turbine equipped with a synchronous generator (SG), in which a full load variable frequency control (VFC) is connected directly between the generator stator and the grid, the VFC of the DFIG is smaller in size and therefore much cheaper. In the DFIG concept, the induction generator (IG) is grid-connected 978-1-4673-0102-2/11/$26.00 ©2011 IEEE 425 A23 2011 IEEE Student Conference on Research and Development (SCOReD) (Pb) as well as the best position of its neighbours (Pg), and then compute a new position that the ‘‘particle’’ is to fly to. Assuming the dimension of a searching space to be D, the total number of particles is n, the position of the ith particle can be expressed as vector Xi = (xi1,xi2, . . .,xiD); the best position of the ith particle being searched until now is denoted as Pib = (pi1,pi2, . . .,piD), and the best position of the total particle swarm being searched until now is denoted as vector Pg = (pg1,pg2, . . .,pgD); the velocity of the ith particle is represented as vector Vi = (vi1, vi2. . . viD). Then the original PSOA is described as [19]: swarm optimization (PSO) was demonstrated to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. In this paper, the hybrid PSO-GA algorithm is used to find the optimal parameters of the various PI controllers for the rotor and grid side converters of the variable frequency converter simultaneously. II. GENETIC ALGORITHM (GA) The GA is a search algorithm based on the mechanism of natural selection and natural genetics [13]. In a simple GA, individuals are similar to a chromosome that codes for the variables of the problem. The strength of an individual is the objective function that must be optimized. Population of candidates evolves by genetic operators: mutation, crossover, and selection. The characteristics of good candidates have more chances to be inherited, because good candidates live longer. So the average strength of the population rises through the generations. Finally, the population stabilizes, because no better individual can be found. At that stage, the algorithm has converged, and most of the individuals in the population are identical, and represent a suboptimal solution to the problem. A GA is governed by three factors: the mutation rate, the crossover rate, and the population size. The implementation of GA is detailed in [14]. GA is one of the effective methods for optimization problems especially in non-differential objective functions with discrete or continues decision variables [15]. As with any search algorithm, the optimum solution is obtained only after much iteration. The speed of the iterations is determined by the length of the chromosome and the size of the populations. There are two main methods for GA to generate itself, namely generational and steady state. In generational, an entire population is replaced after iteration (generation). Whereas in steady state, only a few members of the population are discarded at each generation, and the population size remains constant [16]. One of the drawbacks of GA is its possibility to converge prematurely to a suboptimal solution [17]. Another drawback of this algorithm is its high sensitivity to initial population [16, 18]. There are a few main limitations of a GA when applied to problems [16] vid (t + 1) = vid (t ) + c1 * rand() *[ pid (t ) − xid (t )] + (1) c2 * rand() *[ pgd (t ) − xid (t )] xid (t + 1) = xid (t ) + vid (t + 1) 1≤ i ≤ n (2) 1≤ d ≤ D Where c1, c2 are the acceleration constants with positive values; rand is a random number between 0 and 1; w is the inertia weight. In addition to the parameters c1, and c2 parameters, the implementation of the original algorithm also requires placing a limit on the velocity (Vmax). After adjusting the parameters w and Vmax, the PSO can achieve the best search ability. The adaptive particle swarm optimization (APSO) algorithm is based on the original PSO algorithm, firstly proposed by Shi and Eberhart in 1998 [20]. The APSO can be described as follows: vid (t +1) = w*vid (t) + c1 *rand()*[ pid (t) − xid (t)]+ c2 *rand()*[ pgd (t) − xid (t)] xid (t + 1) = xid (t ) + vid (t + 1) 1≤ i ≤ n 1≤ d ≤ D (3) In which w is a new inertial weight. These algorithms can reduce w gradually as the generation increases by adjusting the parameter w. In the searching process of the PSO algorithm, the searching space will reduce gradually as the generation increases. So the APSO algorithm is more effective, because the searching space reduces step by step nonlinearly, so the searching step length for the parameter w here reduces correspondingly. Similar to GA, after each generation, the best particle of particles in the last generation will replace the worst particle of particles in current generation, thus a better result can be achieved. Generally, in the beginning stages of algorithm, the inertial weight w should be reduced rapidly, around optimum, the inertial weight w should be reduced slowly [20]. 1. The fitness function must be well-written 2. It is a blind and undirected search 3. It is a stochastic search 4. It is sensitive to initial parameters 5. It is computationally expensive 6. What is the stopping criterion? III. PARTICLE SWARM OPTIMIZATION (PSO) Particle swarm optimization (PSO) is a kind of algorithm to search for the best solution by simulating the movement and flocking of birds. The algorithm works by initializing a flock of birds randomly over the searching space, in which every bird is called a ‘‘particle’’. These ‘‘particles’’ fly at a certain velocity and find the global best position after some iteration. At each iteration, each particle can adjust its velocity vector, based on its momentum and the influence of its best position IV. TUNING THE PARAMETERS OF THE PI CONTROLLERS USING HYBRID PSO-GA: In the wind turbine equipped with the DFIG, the variable frequency converter and its power electronics (IGBT- 426 A23 2011 IEEE Student Conference on Research and Development (SCOReD) Figure 1. Effect of grid-side PI controllers transient disturbances [22]. However, tuning controllers is difficult to achieve a set of optimal parameters manually. In this section, the PSO-GA algorithm is applied to find the optimal parameters of the RSC and GSC controllers simultaneously. The genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population [21]. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. However if the initial population is well selected, the performance of the algorithm may enhance. PSO, on the other hand, is not as sensitive as GA to initial population. One of the characteristics of PSO is its fast convergence towards global optima in the early stage of the search and its slow convergence near the global optima. The idea behind this paper is the combination of the PSO and GA algorithm in such a way that the performance of the newly switches) are the most sensitive part. The converter action will probably determine the operation of the wind turbine during transient disturbances in the power grid. Grid faults, for example, even far away from the location of the wind farm, can cause voltage sags at the connection point of the wind turbine. This voltage sag will result in an imbalance between the turbine input power and the generator output power and therefore a high current in the stator windings of the DFIG. Because of the magnetic coupling between stator and rotor, this current will also flow in the rotor circuit and the converter. Since the power rating of the IGBT converter is only 25-30% of the induction generator power rating, this over-current can lead to the destruction of the converter. On the other hand, the behavior of the converter depends on the control system. If the controllers are tuned properly, it is possible to limit the over current of the rotor circuit and therefore improve the converter’s performance during the TABLE I. PI PARAMETERS OF CASE1 Initial design Optimal design kP Vdc kI Vdc kP kI kP kI kP kI grid grid Q Q rotor rotor 0.0001 0.15 3 80 0.07 7 0.4 0.07 0.0278 0.4957 1.9234 269.01 0.07 7 0.4 0.07 427 A23 2011 IEEE Student Conference on Research and Development (SCOReD) Figure 2. Effect of rotor-side PI controllers established algorithm is better than the PSO or GA algorithm. The objective of the PSO is to find the optimal parameters of the PI controllers. Generally, the PI controller performance in the time domain can be measured by a set of parameters: the overshoot Mp, the rise time tr, the settling time ts, and the steady-state error Ess. In this paper, the objective is to reduce the over-current in the rotor circuit during the grid faults. Therefore, a performance measure function is defined as follows [23]: Case1. Effect of grid-side PI controllers In this case the effect of grid side PI controllers on rotor current overshoot is considered. By tuning the parameters of grid side PI controllers ([Kp,vdc, KI,vdc, KP,grid, KI,grid]) using PSO-GA a 30.04% reduction is obtained (Fig.6). Following Table 1 shows the tuned parameters. Case2. Effect of rotor-side PI controllers In this case the effect of rotor-side PI controllers on rotor current overshoot is considered. By tuning the parameters of rotor side PI controllers ([Kp,Q, KI,Q, KP,rotor, KI,rotor]) using PSO-GA a 29.86% reduction in rotor current overshoot is obtained (Fig.7). Following Table 2 shows the tuned parameters. Case3. Effect of All PI controllers f ( x) = c1.ΔI r , max + (1 − c1 ).(t s − t0 ) + c2 . E ss V. SIMULATION AND DISCUSSIONS TABLE II. PI PARAMETERS OF CASE2 Initial design kP Vdc kI Vdc kP kI kP kI kP kI grid grid Q Q rotor rotor 0.0001 0.15 3 80 0.07 7 0.4 0.07 0.0001 0.15 3 80 0.0032 8.12 1.72 9.072 Optimal design 428 A23 2011 IEEE Student Conference on Research and Development (SCOReD) [9] In this case the effect of rotor-side and grid side PI controllers on rotor current overshoot is considered. By tuning the parameters of rotor-side and grid-side PI controllers simultaneously ([Kp,vdc, KI,vdc, KP,grid, KI,grid , Kp,Q, KI,Q,KP,rotor, KI,rotor]) using PSO-GA a 30.06% reduction in rotor current overshoot is obtained (Fig.8). [10] [11] V. CONCLUSION Wind farm is represented by an aggregated model in which hundreds of individual wind turbines and DFIGs are modelled as one equivalent DFIG driven by a single equivalent wind turbine. In this paper, the effect of rotor-side controllers, grid-side controllers, and all PI controllers on over current in the rotor circuit is studied. The hybrid particle swarm optimization and Genetic algorithm (PSO-GA) is then used to find the optimal parameters of the PI controllers for both the RSC and GSC. The PSO-GA is approximately 3 times faster than RCGA and the accuracy of PSO-GA is better. The results show that PI controller of Vdc regulator is the most effective part in reducing the over current of the rotor circuit. And PI controllers of the grid-side converter reduced the over current by 30.04% while PI controllers of the rotorside converter reduced the over current by 29.86%. [12] [13] [14] [15] [16] [17] [18] [19] ACKNOWLEDGMENT [20] This work was partially supported by the Malaysian Government under FRGS grant Vot No. 78460. [21] REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] V. Akhmatov, Analysis of Dynamic Behavior of Electric Power Systems with Large Amount of Wind Power, Ph.D. thesis, Technical University of Denmark, Kgs. Lyngby, Denmark, Apr. 2003. M. V. A. Nunes, J. A. Pecas Lopes, H. H. Zurn, U. H. Bezerra, and R. G. Almeida, “Influence of the variable-speed wind generators in transient stability margin of the conventional generators integrated in electrical grids,” IEEE Trans. Energy Conversion, vol. 19, no. 4, Dec. 2004, pp. 692-701. T. Ackermann and L. S¨oder, “An overview of wind energy-status 2002” Renew. Sustain. Energy Rev., vol. 6, no. 1–2, pp. 67–128, Feb./Apr. 2002. Nordex. (2005, Jan.) N80/2500 kW N90/2300 kW. Brochure. [Online]. Available: http://www. Nordex –online .com /e /online service/ download/ dateien/PB N80 GB.pdf [5] (2005, Jan.) V1204.5 MW Offshoreleadership. Brochure. [Online]. Available: http://www.vestas.com/pdf/produkter/AktuellBrochurer/v120/V12 0%20UK.pdf M. Lown, E. Swidenbank, B. W. Hogg, “Adaptive fuzzy logic control of a turbine generator system,” IEEE Trans. Energy Conversion, vol. 12, no. 4, Dec. 1997, pp. 394-399. Y.-Y. Hsu, C.-H. Cheng, “Variable structure and adaptive control of a synchronous generator,” IEEE Trans. Aerospace and Electronic Systems, vol. 24, no. 4, July 1988, pp. 337-345. G. K. Venayagamoorthy, R. G. Harley, and D. C. Wunsch, “Comparison of heuristic dynamic programming and dual heuristic programming adaptive critics for neurocontrol of a turbogenerator,” IEEE Trans. Neural Networks, vol. 13, no. 3, May 2002, pp. 764- 773 J. Morren and S. W. H. de Haan, “Ridethrough of wind turbines with doublyfed induction generator during voltage dip,” IEEE Trans. Energy Conversion, vol. 20, no. 2, Jun. 2005, pp. 435-441. [22] [23] [24] [25] R. Pena, J. C. Clare, and G. M. Asher, “Doubly fed induction generator using backto- back PWM converters and its application to variable-speed wind-energy generation,” IEE Proceedings – ElectRic Power Applications, vol. 143, no. 3, May 1996, pp. 231241. M. A. Abido, “Robust design of multimachine power system stabilizers using simulated annealing,” IEEE Trans. Energy Conversion, vol. 15, no. 3, Sept. 2000, pp. 297-304 D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, New York: IEEE Press, 2000, ISBN 0-7803-5379-X. K.F.Man, K.S.Tang and S.Kwong, "Genetic Algorithm concepts and application", IEEE Trans.On Industrial Electronics, vol 43 no.5, pp.519-534,Oct 1996. J.H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: The University of Michigan Press, 1975. T.S. Chung, Y.Z. Li," A Hybrid GA Approach for OPF with Consideration of FACTS Devices", IEEE Power Engineering Review, February 2001 [ Betram Koh Lin Hon "Accelerated Genetic Algorithm in Power System Planning" Electrical Engineering thesis 2003 Stephane Gerbex, Richard Cherkaoui and Alain.J.Germond "Optimal location of multi-type FACTS devices by means of Genetic algorithm" IEEE Trans. Power system Vol.16 pp 537-544 August 2001 Jose Miva, Jose Ramon, Alvarez "Artificial Intelligence and Knowledge Engineering Applications" Ebook James Kennedy and Russel Eberhart, "Particle Swarm Optimization" Proc. of IEEE International conference on neural networks, Vol 14, pp 1942 – 1948 December 1995 Yuhui Shi, Russel.C.Eberhart," Empirical study of particle swarm optimization", Proc. of the congress on Evolutionary computation, Vol.13, pp 1945-1950, July 1999 Mohammadi, M. Jazaeri, "A hybrid particle swarm optimizationgenetic algorithm for optimal location of SVC devices in power system planning", M. Fendereski, A. Mohammadi, M.T. Arabyarmohammadi, S.Amirkhan, M. Sadighi, "Response Improvement of Doubly Fed Induction Generators Driven by Wind Turbines Using PSO and RCGA Algorithms" Wei Qiao; Venayagamoorthy, G.K.; Harley, R.G. "Design of Optimal PI Controllers for Doubly Fed Induction Generators Driven by Wind Turbines Using Particle Swarm Optimization", Neural Networks, 2006. IJCNN; International Joint Conference on Neural Networks, Page(s):1982-1987, 10.1109/IJCNN 2006. M. Eidiani and M.H.M. Shanechi, “FAD-ATC, A New Method for Computing Dynamic ATC”, International Journal of Electrical Power & Energy System,vol. 28, n. 2, pp. 109-118, Feb. 2006. M. Eidiani, “A Reliable and efficient method for assessing voltage stability in transmission and distribution networks”, International Journal of Electrical Power and Energy Systems, vol.33, n.3, pp. 453-456, March 2011. H.Zeynal, A.K.Zadeh, K. M. Nor, M. Eidiani, “Locational Marginal Price (LMP) AssessmentUsing Hybrid Active and Reactive Cost Minimization”, International Review of Electrical Engineering (I.R.E.E.),vol.5, n.5, pp.2413-2418, October 2010 APPENDIX Induction generator: rated power =10MW, Rated voltage = 575 V, Rs = 0.00706 pu, Lls = 0.171 pu, Rr = 0.005 pu, Llr = 0.156 pu, Lm = 2.9 pu, H = 5.04 sec. Converter: PWM frequency = 1620 Hz, Rated voltage DC = 1200 V, Capacitor DC link= 6e-4 Farads Power network: 2500MVA, 120KV base, R1 = R2 = 0.1153 ohms/Km, L1 = L2 = 1.05 e-3 H/Km, line Length = 30 km. 429