Dynamic Simulation of Power Systems with Grid Connected Windfarms 239 Fig. 19 and 20 show the response of the induction generator terminal voltage with SVC/TCSC and STATCOM. /UPFC. It can be concluded that terminal voltage of the DFIG is above 0.75 p.u. after 0.1 seconds. Comparing Fig. 18, 19 and 20 it can be concluded that the UPFC improves the fault ride through capability of the DFIG very effectively. Fig. 19. Induction Generator Terminal Voltage – Effect of SVC and TCSC Fig. 20. Induction Generator Terminal Voltage – Effect of STATCOM and UPFC 4.5 Effect of wind speed variations The dynamic performance of the FACTS controllers with doubly fed induction generator (DFIG) based wind farm is investigated using the wind speed model shown in Fig. 21.[7] 0.85 0.90 0.95 1.00 1.05 0123 4 5 Time (sec) SVC TCSC Terminal Voltage (p.u) 0.92 0.94 0.96 0.98 1.00 1.02 0123 4 5 Time ( sec ) STATCOM UPFC Terminal Voltage (p.u) Wind Farm – Impact in Power System and Alternatives to Improve the Integration 240 Fig. 21. Wind speed Model considered for long term dynamic simulation The average wind speed is around 5 Km/h approximately. The wind speed data are obtained by measuring the wind speed changes over an hour from the regional meteorological website. It can be observed that during the time period from 0- 1000 sec the wind speed fluctuates around an average wind speed of 5 Km/h. But the wind speed reaches 16 Km/h around 1,200 seconds. The corresponding rotor speed variation by the induction generator is shown in Fig 22. It can be observed that the rotor speed changes from its initial value to 1.25 p.u. following wind speed increase at 1200 seconds. Time (Sec) Fig. 22. Rotor Speed response of DFIG The corresponding active power variations are shown in Fig.23. W ind Speed (Km/h) Rotor Speed (p.u.) Dynamic Simulation of Power Systems with Grid Connected Windfarms 241 Time (Sec) Fig. 23. Active Power Injected by the wind farm The active power variations following the wind speed changes are highly fluctuating from the steady state load flow level to the grid. The performance coefficient C p of the wind turbine is kept as 0.48 in the algebraic equation 1 C.ρ.AV 2 3 p P = . Fig. 24 shows the impact of an SVC/STATCOM controller on the rotor speed response of the DFIG. Fig. 24. Rotor Speed Response of induction Generator with SVC/STATCOM There are no significant rotor speed oscillations in the rotor speed of the induction generator with SVC in the network; however the rotor speed increases to 1.26 p.u. with SVC in the network following wind speed increase of 16 Km/h near 1200 seconds. The rotor speed response of induction generator with TCSC/UPFC is shown in Fig 25. 0 200 400 600 800 1000 1200 1.08 1.10 1.12 1.14 1.16 1.18 1.20 1.22 1.24 1.26 Rotor Speed (p.u.) Time(Sec) SVC STATCOM Active Power Injected M W Wind Farm – Impact in Power System and Alternatives to Improve the Integration 242 It can be noticed that the rotor speed oscillations are damped effectively with UPFC in the network. Fig. 25. Rotor speed Response of Induction generator with TCSC/UPFC. 5. Conclusion For the simulation study the gains and time constants of the FACTS controllers are tuned using a conventional optimization program, which minimizes the voltage /rotor speed oscillations of the induction generator. Among series connected FACTS controllers the UPFC damps both rotor angle oscillations of synchronous generators and rotor speed oscillations of induction generator very effectively when compared with TCSC. This is due to the reactive support provided by the shunt branch of the UPFC following the disturbance. However the reactive power rating of UPFC is very high compared to that of the TCSC .It is suggested that a STATCOM of suitable rating may be installed at the point of common coupling (PCC) with or without a capacitor may be used for stabilizing rotor speed oscillations associated with doubly fed variable speed induction generators following transient faults and disturbances. The development of wind turbine and wind farm models is vital because as the level of wind penetration increases it poses dynamic stability problems in the power system. For the 0200 400 600 800 1000 1200 1.09 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 Rotor Speed (p.u.) Time(Sec) TCSC UPFC Dynamic Simulation of Power Systems with Grid Connected Windfarms 243 present work we have taken a taken a doubly fed induction generator model and illustrated the presence of sustained oscillations with wind farms. Suitable Flexible A.C.Transmission Systems controllers are modeled using the non-linear simulation models and the transient ratings of the FACTS controller are obtained to stabilize the rotor speed/rotor angle oscillations in a DFIG based wind energy conversion scheme. The rotor speed stability of the DFIG based system following a generator outage is studied. It can be observed that the effect of low voltage ride through (LVRT) is very minimum following the contingency and the presence of a FACTS device like the SVC improves the rotor speed stability. This chapter also presented the results of a long term dynamic simulation of a grid connected wind energy conversion system which simulated wind speed changes. From the results it is observed that STATCOM and UPFC are effective candidates for damping the rotor speed oscillations of the induction generator. 6. Appendix a. Parameters Base values for the per unit system conversion. Base Power: 100 MVA, Base Voltage: 0.69 KV for low voltage bus bar, 150 KV for high voltage busbar. b. Doubly- Fed Induction Generator Rated apparent power MVA: 2 MVA, Rotor inertia: 3.527 MW s/MVA R s (p.u.) = 0.0693,X s (p.u.) = 0.080823,R r (p.u.) =0.00906,X r (p.u)= 0.09935, X m (p.u) = 3.29, Minimum Rotor Speed: 0.56 p.u., Maximum Rotor Speed: 1.122 p.u. c. Transformers Three winding transformer (150 KV: 0. 69 KV), Primary rated apparent power=25 MVA, Secondary rated apparent power = 25MVA,Tertiary rated apparent power = 6 MVA. 7. Acknowledgement The author sincerely thanks Dr.M.Abdullah Khan, Professor of Eminence/EEE, B.S.Abdur Rahman University (Formerly B.S.A.Crescent Engineering College) for his invaluable guidance for completing the research work. The author sincerely thanks his father Mr. S.K.Natarajan & wife Mrs. Bhuvana for the moral support extended to him, at times of pressure during the research work. The author also wishes to place on record his sincere gratitude to Mr.R.M.Kishore Vice Chairman, RMK Engineering College and Prof.Geetha Ramadas, Head of the Department, Electrical and Electronics Engineering, RMK Engineering College, Tiruvallur District, Tamilnadu, India. 8. References Abdel – Magid Y.L. and El-Amin I.M., (1987)“Dynamic Stability of wind –turbine generators under widely varying load conditions”, Electrical Power and Energy Systems, Vol.9, No.3, pp.180-188,1987. Chai Chompoo-inwai, Wei-Jen Lee, Pradit Fuangfoo, Mitch Williams and James. R.Liao, (2005), “System Impact study for the interconnection of wind generation and utility system”, IEEE Transactions on Industry Applications, Vol.41, No.1, pp.163- 168 Wind Farm – Impact in Power System and Alternatives to Improve the Integration 244 Claudio A. Canizares, Massimo Pozzi, Sandro Corsi, Edvina Uzunovic,(2003), “STATCOM modelling for voltage and angle stability studies”, Electrical Power and Energy Systems , Vol.25,pp.431-441. Clemens Jauch, Poul Sorensen, Ian Norheim, Carsten Rasmussen, (2007) “Simulation of the Impact of Wind power on the transient fault behavior of the Nordic Power System”, Electric Power System Research, Vol. 77, pp.135-144. Haizea Gaztanaga, Ion Etxeberria-Ottadui, Dan Ocnasu,(2007) “Real time analysis of the transient response improvement of wind farms by using a reduced scale STATCOM prototype”, IEEE Transactions on power systems, Vol.22, No.2, pp.658- 666. http://www.kea.metsite.com-online website for wind speed data. Istvan Erlich, Jorg Kretschmann, Jens Fortmann, Stephan Mueller-Engelhardt and Holger Wrede, (2007),“Modeling of Wind Turbines Based on Doubly-Fed Induction Generators for Power System Stability Studies”, IEEE Transactions on Power Systems, Vol.22, No.3, 2007, pp.909-919, 2007. Kundur.P,(1994), “ Power System Stability and Control”, McGraw hill. Lie Xiu, Yi Wang,(2007), “Dynamic Modeling and Control of DFIG based Wind Turbines under Unbalanced Network Conditions , IEEE Transactions on Power Systems, Vol.22, No.1, pp. 314-323 Mohamed S.Elmoursi, Adel M.Sharaf,(2006), “Novel STATCOM controllers for voltage stabilization of standalone Hybrid schemes”, International Journal of Emerging Electric Power Systems, Vol.7, Issue 3, Art 5,pp. 1-27. Mohan Mathur R, Rajiv .K Varma, (2002), Thyristor – Based FACTS controllers for electrical transmission systems , IEEE press, Wiley and Sons Publications Nadarajah Mithulananthan, Claudio A. Canizares, Graham J.Rogers ,(2003), “Comparison of PSS, SVC and STATCOM controllers for Damping Power System Oscillations”, IEEE Transactions on Power Systems, Vol.8, No.2, pp.786-792. Olof Samuelsson and Sture Lindahl, (2005),“On Speed Stability”, IEEE Transactions on Power Systems, Vol.20, No.2,pp. 1179-1180. Senthil Kumar.N. , Abdullah Khan.M., (2008) ,“Impact of FACTS controllers on the dynamic stability of power systems connected with Wind Farms”, Wind Engineering, Vol.32, No.2, pp.115-132. Varma R.K. and Tejbir S.Sidhu, (2006)“Bibliographic Review of FACTS and HVDC applications in Wind Power Systems”, International Journal of Emerging Electric Power Systems, Vol.7, No. 3, pp. 1-16 Vladislav Akhmatov, (2003),“Analysis of dynamic behavior of electric power systems with large amount of wind power”, Ph.D thesis, Technical University of Denmark Dr.N.SENTHIL KUMAR is presently working as Professor in the department of Electrical and Electronics Engineering, RMK Engineering College, Chennai. His area of research includes modeling of FACTS devices for power system studies, modeling of wind energy conversion systems for power system stability analysis. Email: nsksai@rediffmail.com Part 3 Modelling and Simulation of Wind Power System 11 Modeling Wind Speed for Power System Applications Noha Abdel-Karim, Marija Ilic and Mitch J. Small Carnegie Mellon University USA 1. Introduction The intermittent nature of wind power presents special challenges for utility system operators when performing system economic dispatch, unit commitment, and deciding on system energy reserve capacity. Also, participation of wind power in future electricity markets requires more systematic modeling of wind power. It is expected that the installed energy capacities from wind sources in the United States will increase by up to 20% by the year 2020. New York Independent System Operator (NYISO), General Electric (GE), and Automatic Weather Stations Inc., (AWS) conducted a project for the future of wind energy integration in the United States. They stated that NY State has 101 potential wind energy sites and it should be able to integrate wind generation up to at least 10% of system peak load without further expansion (GE report 2005). In order to integrate wind power systematically, it is necessary to solve the technical challenges as well as policy regulation designs. Some of these polices have been updated to allow increased intermittent renewable energy by settling imbalances in generation rulemakings and portfolio standards, where the most commonly used one at this time is the production tax credit portfolios. Due to intermittent nature of wind power, forecasting methods become a powerful tool and of great importance to many power system applications that include uncertainties in generation outputs. The recent work has discussed several methods to develop wind power forecasting algorithms to anticipate the degrees of uncertainty and variability of wind generation. (C. Lindsay & Judith, 2008) use an auto-regressive moving average model to estimate the next ten-minute ahead production level for a hypothetical wind farm and investigate the possibility of pairing wind output with responsive demand to reduce the variability in the net wind output. In (Kittipong M. et al., 2007), the authors develop an Artificial Neural Network (ANN) model to forecast wind generation power with 10-min resolution. Current and previous wind speed and wind power generation are used as input parameters to the network where the output from the ANN is the wind generation power. (M. S. Miranda & R. W. Dunn, 2006) predicted one-hour-ahead of wind speed using both an auto-regressive model and Bayesian approach. (D. Hawkins & M. Rothleder, 2006), discuss operational concerns with increased amount of wind energy in the Day-ahead- and Hour- ahead-Market for CAISO in California. They emphasize the importance of forecasting accuracy for unit commitment and ancillary services and the implications of load following or supplemental energy dispatch to rebalance the system every five minutes. In (Alberto F. at el., 2005), the authors propose a probabilistic method to estimate the forecasting error for Wind Farm – Impact in Power System and Alternatives to Improve the Integration 248 a Spanish Electricity System. They propose cost assessment with wind energy prediction error. The assessment is developed to estimate the cost associated with any energy deviation they cause. (Dale L. Osborn, 2006) discusses the impact of wind on the LMP market for Midwest MISO at different wind penetrations level. His LMP calculations decrease with the increase of wind energy penetration for the Midwest area. The authors of (Cameron W. Potter at el., 2005) describe very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia. They introduce an Adaptive Neural Fuzzy Inference System (ANFIS) for short-term forecasting of a wind time series in vector form that contains both wind speed and wind direction. We next describe our modeling approach to derive a family wind models ranging from short through and long term models. Using the same data, we illustrate achievable accuracy of this model. This chapter presents three major parts in sections 2, 3 and 4. First, section 2 presents a short term wind speed linear prediction model in state space representation using linear predictive coding (LPC), FIR and IRR filters. 10-minute, one-hour, 12-hour, and 24- hour wind speed predictions are evaluated in least square error sense and the prediction coefficients are then used in the state space stochastic formula representing past and future predicted values. One year wind speed data in 10 minute resolution are first fitted by two Weibull distribution parameters and then transformation to normal distribution is done for prediction calculation purposes. Second, section 3 of the chapter models wind speed patterns by decomposing it in different time scales / frequency bands using the Fourier Transform. The decomposition ranges from hourly (high frequency) up to yearly (low frequency), and are important in many power grid applications. Short, medium and long-term wind speed trends require data analysis that deals with changing frequencies of each pattern. By applying Fourier analysis to wind speed signal, we aim to decompose it into three components of different frequencies, 1) Low Frequency range: for economic development such as long term policies adaptation and generation investment (time horizon: many years), 2) Medium Frequency range: for seasonal weather variations and annual generation maintenance (time horizon: weeks but not beyond a year), 3) High frequency range: for Intra-day and Intra-week variations for regular generation dispatches and generation forced outage (time horizon: hours but within a week). Each decomposed signal is presented in a lognormal distribution model and a Discrete Markov process and the aggregated complete wind speed signal is also applied. Third, section 4 presents the prediction results using past histories of wind data, which support validity of Markov model. These independencies have been modeled as linear state space discrete Markov process. A uniform quantization process is carried to discretize the wind speed data using an optimum quantization step between different state levels for both wind speed distributions used. Also state and transition probability matrices are evaluated from the actual representation of wind speed data. Transition probabilities show smooth transitions between consecutive states manifested by the clustering of transition probabilities around the matrix diagonal. 2. Wind speed prediction model 2.1 Wind data distribution models This prediction model uses more than 50 thousands samples of one-year wind speed data in 10-minute resolution. The data are used to determine the best fitted parameters of the [...]... minutes and the measurement delay is 1 hour, then the sample count delay is L = 60/10 = 6 samples The minimum estimation delay is L = 1 In our work we excluded the white noise generation alternative and considered the two other alternatives for wind speed forecasting 254 Wind Farm – Impact in Power System and Alternatives to Improve the Integration 2.6 The prediction algorithm for wind speed 2.6.1 Linear... use in different applications in power systems, e.g., wind power predictions, scheduling and investment decisions 259 Modeling Wind Speed for Power System Applications I xH(t) xt ( t )  x H ( t )  x M ( t )  x L ( t ) II xM(t) III xL(t) Fig 10 Construction of wind speed signal using low, medium and high frequency components 260 Wind Farm – Impact in Power System and Alternatives to Improve the Integration. .. the 256 Wind Farm – Impact in Power System and Alternatives to Improve the Integration prediction The 10-minute wind speed prediction model shows persistence for all prediction orders used 2 1.5 Normalized Wind speed 1 0.5 0 -0.5 -1 -1.5 0 2 4 6 8 10 min 10 12 Hour 14 1 hour 16 18 20 22 24 Actual Fig 8 Ten min and one hour prediction using 1 hour past values The effect of how the increase in the number... 250 Wind Farm – Impact in Power System and Alternatives to Improve the Integration This transformation is used in both the fitting and prediction processes The histograms of wind speed signals in both Weibull and Normal distributions are shown in Figures 2 and 3, respectively By looking to Figure 3, the shape of the actual signal is shifted down with the exact pattern due to the normalization process,... X[k], is computed for the natural logarithm of wind speed signal, x[n] The DFT is then decomposed in frequency domain into low, medium and high frequency components, each of different frequency index range as: 258 Wind Farm – Impact in Power System and Alternatives to Improve the Integration X[ k ]  XL [ k ]  X M [ k ]  X H [ k ] (12) Where XL[k], XM[k] and XH[k] are the low, medium and high frequency... filter) 252 Wind Farm – Impact in Power System and Alternatives to Improve the Integration Where δ(n) is the Kronecker delta function The wind speed random process x(n) is characterized as wide sense stationary (WSS) Gaussian (Normal) process, and hence will remain Gaussian after any stage of linear filtering However, the wind speed process is NOT white but can be closely modeled as Auto-Regressive... By taking the exponent of each signal in (15), we obtain the log-normal time domain signals that represent the low, medium and high frequency components of the original wind speed signal Each pattern can be used to characterize the behavior of wind speed for different purposes Figure 10 shows the aggregation of the three log-normal wind speed components in the time domain Each decomposed wind speed... performed for the sole purpose of prediction, for both the fitting and prediction processes Figures 2 and 3 show the histograms and time series, respectively, for both the actual (Weibull) wind speed X and Normal wind speed Xn The shape of the Normal signal Xn is shifted down with negative values (Figure 3) compared to the actual signal X due to the normalization process 2.3 Linear prediction and filter... states and suggesting that the data does not exhibit frequent wind gusts Moreover, we see the difference between theoretical and actual (statistical state probabilities (Figures 17 & 18) The reason is due to the use of the uniform quantization while we conjecture that a non-uniform quantizer will achieve a better match between the actual and theoretical probabilities 262 Wind Farm – Impact in Power System. .. design ˆ To predict the normalized wind speed data, a forward LPC predictor xLPC (n) can certainly be used, but its accuracy is rather poor However, the main advantage of LPC is that, as the prediction order N increases sufficiently, the prediction error eN(n) tends to be closely approximated as white noise (P P Vaidyanathan, 2008) This helps in modeling the Normal 253 Modeling Wind Speed for Power System . one. Wind Farm – Impact in Power System and Alternatives to Improve the Integration 250 This transformation is used in both the fitting and prediction processes. The histograms of wind speed. excluded the white noise generation alternative and considered the two other alternatives for wind speed forecasting. Wind Farm – Impact in Power System and Alternatives to Improve the Integration. minutes. In (Alberto F. at el., 2005), the authors propose a probabilistic method to estimate the forecasting error for Wind Farm – Impact in Power System and Alternatives to Improve the Integration