A Neural Network Based Wind Speed Estimator for a Wind Turbine Control

Transcription

1 A Neural Network Based Wind Speed Estimator for a Wind Turbine Control Oscar Barambones +, Jose Maria Gonzalez de Durana and Enrique Kremers Department of Automatic Control, University of the Basque Country EUI de Vitoria. Nieves cano Vitoria. (Spain) European Institute for Energy Research (EIFER). Universitat Karlsruhe + oscar.barambones@ehu.es Abstract Variable speed wind generation systems are more attractive than fixed-speed systems because of the more efficient energy production improved power quality, and improved dynamic performance during grid disturbances. In this sense, to implement maximum wind power extraction, most controller designs of the variable-speed wind turbine generators employ anemometers to measure wind speed in order to derive the desired optimal shaft speed for adjusting the generator speed. In this paper it is proposed a new Neural Network Based Wind Speed Estimator for a wind turbine control. The design uses an feedforward Artificial Neural Network (ANN) to implement a rotor speed estimator, and simulated results show that the proposed observer provides high-performance dynamic characteristics. I. INTRODUCTION The technology of extracting energy from the wind has evolved dramatically over the last few decades. To understand what was happening, it is necessary to consider four main factors. First of all there was a need. An emerging awareness of the finiteness of the earth s fossil fuel reserves as well as of the adverse effects of burning those fuels for energy had caused many people to look for alternatives like wind energy. Second, there was the potential. Wind exists everywhere on the earth, and in some places with considerable energy density. Third, there was the technological capacity. Fourth, the government support. Is was required to carry out research, development, and testing; to provide regulatory reform to allow wind turbines to interconnected with electrical networks and to offer incentives because the cost of energy from wind turbines was far higher than that from fossil fuels. The necessary political will for this purpose appeared at different times and to varying degrees, in a number of countries: first in the United States, Denmark and Germany, and now in much of the rest of the world. Remarkable advances in the wind power design have been achieved due to modern technological developments. Since 1980, advances in aerodynamics, structural dynamics, and micrometeorology have contributed to a 5% annual increase in the energy yield of the turbines. Current research techniques are producing stronger, lighter and more efficient blades for the turbines. The annual energy output for turbine has increased enormously and the weights of the turbine and the noise they emit have been halved over the last few years. We can generate more power from wind energy by establishment of more number of wind monitoring stations, selection of wind farm site with suitable wind electric generator, improved maintenance procedure of wind turbine to increase the machine availability, use of high capacity machine, low wind regime turbine, higher tower height, wider swept area of the rotor blade, better aerodynamic and structural design, faster computer-based machining technique, increasing power factor and better policies from Government. The worldwide wind power installed capacity reaches MW in 2008, and it is expected an annual growth rate of MW in 2009 and MW in 2010 [18]. Wind energy is expected to play an increasingly important role in the future national energy scene [2], [3]. Wind turbines convert the kinetic energy of the wind to electrical energy by rotating the blades. Greenpeace states that about 10% electricity can be supplied by the wind by the year At good windy sites, it is already competitive with that of traditional fossil fuel generation technologies. With this improved technology and superior economics, experts predict wind power would capture 5% of the world energy market by the year Advanced wind turbine must be more efficient, more robust and less costly than current turbines. To implement maximum wind power extraction, most controller designs of the variable-speed Wind Turbine Generators (WTG) employ anemometers to measure wind speed in order to derive the desired optimal shaft speed for adjusting the generator speed. In most cases, a number of anemometers are placed surrounding the wind turbine at some distance to provide adequate wind speed information. These mechanical sensors increase the cost (e.g., equipment and maintenance costs) and reduce the reliability of the overall WTG system [8]. Recently, mechanical sensorless maximum power point tracking controls have been reported in which the wind speed is estimated or the maximum power point is determined without the need of the wind speed information [1], [8], [15]. However, these methods may result in a complex and time-consuming calculation, therefore, reducing system per /10/$ IEEE 1383

2 formance. Artificial neural networks (ANNs) are well known as a tool to implement nonlinear time-varying input-output mapping. In this sense, it is possible to design the estimators of the wind speed using Artificial Neural Networks, which do not require a mathematical model of the system and therefore the performance of this approach do not exhibit any dependence with the modeling errors. In ANN based estimators, if the ANN uses a supervised training technique, then the estimator is based on information available for the training and this information is obtained from system input and output measurements previously calculated for training purposes. On the other hand, it has been proved that Artificial Neural Network can approximate a wide range of nonlinear functions to any desired degree of accuracy under certain conditions [10]. Due to the above mentioned characteristics, in the past few years, active research has been carried out in Artificial Neural Network applied to identification and control of complex dynamical systems [13]. Although diverse neural architecture and learning algorithms can be used, we have chosen a particular one, the multilayer feedforward network and the so-called backpropagation with momentum algorithm which is a gradient descent algorithm of the performance function. Properly trained backpropagation networks tend to give reasonable answers when they are presented with inputs that they have never computed [5]. This paper proposes a new wind speed estimator based on ANN that let us obtain the maximum extraction of energy from the wind for for variable-speed WTGs. This report is organized as follows. The artificial neural network model for the wind speed estimation is introduced in Section 2. Then, the training of the Artificial Neural Network is carried out in Section 3. Then, some simulation results are presented in section 4. Finally, some concluding remarks are stated in Section 5. II. WIND TURBINE SYSTEM MODELLING Figure 1 shows the functional scheme of the wind turbine generator. The main parts of this scheme are the wind turbine, the gearbox and generator. In a typical turbine design, rotor blades are attached to a shaft that runs into a gearbox. The gearbox, or transmission, increases the speed of the blades rotation, from 18 revolutions per minute (RPM) up to 1,800 RPM. The fast spinning shaft turns inside the generator, producing AC (alternating current) electricity. Electricity must be produced at just the right frequency and voltage to be compatible with the utility grid. The speed of the wind hitting the rotors affects how much energy a turbine captures. Modern wind turbines are designed to work most efficiently at wind speeds between 15 and 35 MPH. Because the wind blows stronger than this some of the time, a wind turbine must adapt itself to the prevailing Fig. 1. Functional scheme of the wind turbine generator wind speed to operate most efficiently. There are two basic approaches used to control and protect a wind turbine: pitch-control and stall-control. In pitch-controlled turbines, an anemometer mounted atop the nacelle, or a wind speed estimator constantly checks the wind speed and sends signals to a pitch actuator, adjusting the angle of the blades to capture the energy from the wind most efficiently. On a stall-regulated wind turbine, the blades are locked in place and do not adjust during operation. Instead the blades are designed and shaped to increasingly stall the blades angle of attack with the wind to both maximize power output and protect the turbine from excessive wind speeds. There are relative advantages to both design approaches. A pitch-regulated turbine, for example, is generally considered to be slightly more efficient than a stallregulated turbine. On the other hand, stall-regulated turbines are often considered more reliable because they do not have the same level of mechanical and operational complexity as pitch-regulated turbines. In this paper the first approach is considered, and therefore an anemometer or a wind speed estimator is needed. In this section it is designed an ANN based wind speed estimator for a pitch-controlled wind turbines. The wind speed estimator will provide the wind speed that is necessary for control the wind turbine system in order to optimize the energy production. The aerodynamic model of a wind turbine can be characterized by the well-known C p λ β curves. C p is the power coefficient, which is a function of both tip-speed-ratio λ and the blade pitch angle β. The tip-speed-ratio is defined by: λ = Rw (1) v where R is the blade length in m, w is the wind turbine rotor speed in rad/s, and v is the wind speed in m/s. The C p λ β curves depend on the blade design and are given by the wind turbine manufacturer. Given the power coefficient C p, the mechanical power that 1384

3 the wind turbine extracts from the wind is calculated by [13]. P m (v) = 1 2 C p(λ, β)ρπr 2 v 3 = f(v, w, β) (2) where ρ is the air density in kg/m 3, A r = πr 2 is the area swept by the rotor blades in m 2. As it is well known, given a specific wind speed, there is a unique wind turbine rotational speed to achieve the maximum power coefficient C pm, and thereby extract the maximum mechanical (wind) power. If the wind speed is below the rated value, the wind turbine operates in the variable speed mode, and the rotational speed is adjusted such that remains C p at the C pm point. In this operating mode, the wind turbine pitch control is deactivated. However, if the wind speed increases above the rated value, the pitch control is activated to increase the wind turbine pitch angle to reduce the mechanical power extracted from wind. For a typical wind power generation system, the following simplified elements are used to illustrate the fundamental work principle. The system primarily consists of an aeroturbine, which converts wind energy into mechanical energy, a gearbox, which serves to increase the speed and decrease the torque and a generator to convert mechanical energy into electrical energy. Driving by the input wind torque T m, the rotor of the wind turbine runs at the speed w. The transmission output torque T t is then fed to the generator, which produces a shaft torque of T e at generator angular velocity of w e. Note that the rotor speed and generator speed are not the same in general, due to the use of the gearbox. The mechanical equations of the system can be characterized by [14]: J m ẇ + B m w = T m + T (3) J e ẇ e + B e w e = T t + T e (4) T t w e = Tw (5) where J m and J e are the moment of inertia of the turbine and the generator, B m and B e are the viscous friction coefficient of the the turbine and the generator, T m is the wind generated torque in the turbine, T is the torque in the transmission shaft before gear box, T f is the torque in the transmission shaft after gear box, and T e is the the generator torque, w is the angular velocity of the turbine shaft and w e is the angular velocity of the generator rotor. The relation between the angular velocity of the turbine w and the angular velocity of the generator w e isgivenbythe gear ratio γ: γ = w e (6) w Then, using equations 3, 4, 5 and 6 it is obtained: Jẇ + Bw = T m + γt e (7) with J = J m + γ 2 J e (8) B = B m + γ 2 B e (9) Now we are going to consider the system electrical equations. In this work it is used a double feed induction generator (DFIG). This induction machine is feed from both stator and rotor sides. The stator is directly connected to the grid while the rotor is fed through a variable frequency converter (VFC). In order to produce electrical active power at constant voltage and frequency to the utility grid, over a wide operation range (from subsynchronous to supersynchronous speed), the active power flow between the rotor circuit and the grid must be controlled both in magnitude and in direction. Therefore, the VFC consists of two four-quadrant IGBT PWM converters (rotor-side converter (RSC) and grid-side converter (GSC)) connected back-to-back by a dc-link capacitor [11], [12]. In the stator-flux oriented reference frame, the d-axis is aligned with the stator flux linkage vector ψ s, and then, ψ ds =ψ s and ψ qs =0. This yields the following relationships [7]: where i qs = L mi qr (10) i ds = L m(i ms i dr ) (11) T e = L mi ms i qr (12) Q s = 3 w s L 2 mi ms (i ms i dr ) 2 (13) di qr v dr = r r i dr + σl r dt sw s σl r i qr (14) di qr v qr = r r i qr + σl r (15) ( dt σlr i dr + L 2 m +sw i ) ms s (16) i ms = v qs r s i qs w s L m (17) σ = 1 L2 m L r (18) Since the stator is connected to the grid, and the influence of the stator resistance is small, the stator magnetizing current (i ms ) can be considered constant [11]. Therefore, the electromagnetic torque can be defined as follows: T e = K T i qr (19) where K T is a torque constant, and is defined as follows: K T = L mi ms (20) Using equations (7) and (19) it is obtained the following equation: T m = P m w = Jẇ + Bw + γk T i qr (21) From this equation it is calculated the mechanical power using the turbine speed and the electromagnetic torque. 1385

4 III. NEURAL NETWORK MODEL FOR WIND SPEED ESTIMATION Equation 2 indicates that given the information of the turbine power P m, the wind turbine rotational speed w,andthe blade pitch angle β, the wind speed can be calculated from the nonlinear inverse function of eqn. 2. A commonly used method to implement an inverse function is using a lookup table, however, this method requires much memory space and may result in a time-consuming search for the solution. The Artificial Neural Networks (ANN), are well known as a tool for nonlinear complex time-varying input-output mapping and can be an ideal technique to solve this problem. Therefore, the proposed wind speed estimation algorithm in this paper is build on an ANN-based input-output mapping that approximates the nonlinear inverse function of f(v, w, β). In the proposed design a multilayer feedforward artificial neural network (FANN) was adopted as the neural network paradigm. The neural network has three input signals, the mechanical power P m, the wind turbine rotor speed w and the blade pitch angle β, and one output, ˆv, which is the estimated wind speed. The number of hidden layers and the number of nodes per layer are not definitive. There are no general guidelines for determining a priori which combinations of neurons and hidden layers will perform the best for a given problem. In this problem, the number of hidden layers and the number of neurons in each hidden layer were chosen heuristically on a trial and error basis. The FANN selected has three hidden layers. The first hidden layer has 9 neurons, the second has 11 neurons and the third hidden layer has 15 neurons. These hidden layers have a tansigmoid activation function, and the output layer has a linear activation function. Then the output of the FANN will be, y(k) =Γ 3 ( W 3 Γ 2 ( W 2 Γ 1 ( u(k)+b 1 )+b 2 )+b 3 ) (22) where W 1, W 2 and W 3 are the weight matrices, b 1, b 2 and b 3 are the bias vectors, Γ 1, Γ 2 and Γ 3 are the tansigmoid activation functions, u =[P m,w,β] is the input and y =ˆv is the output of the neural network. The training algorithm selected to train the neural network is the backpropagation with momentum. This algorithm is an extension of the conventional error backpropagation training algorithm. It is based on the minimization principle of a cost function of the error between the desired output and the actual output of a FANN. The minimization is achieved by varying the adjustable parameters of the FANN in the direction of the gradient descent of the cost function. Besides, the momentum term allows a network to respond not only to the local gradient, but also to recent trends in the error surface. Acting like a low-pass filter, momentum allows the network to ignore small features in the error surface. Without momentum the network may get stuck in a shallow local minimum, however with momentum the network can slide through such a minimum [16]. In the backpropagation algorithm it is useful to rearrange the elements of the weight matrices W i and the bias vectors b i into a vector θ which contains all the adjustable parameters of the network. Then, the cost function in the backpropagation algorithm is chosen to be: J k (θ) = 1 k+t 1 [y(n) y d (n)] 2 (23) T n=k where k denotes the time instant, the parameter T is referred to as the update window size and equals the number of time instants over which the gradient of the cost function J is computed, and y d is the desired output of the neural network. The backpropagation algorithm begins by initially assigning small randomly chosen values for the weights and biases, and then during the training process this values are iteratively adjusted to minimize the neural network cost function. The adjustable parameter can be updated following a gradient descendent with momentum procedure, θ(k + T )=θ(k)+δθ(k) (24) where the increment term of the adjustable parameters is Δθ(k) = α J k(θ) + μδθ(k T ) (25) θ where α is the learning rate and μ is the momentum constant, and the partial derivatives of J with respect to an adjustable parameters θ is given by, J k (θ) θ = 2 T k+t 1 n=k [y(n) y d (n)] y(n) θ (26) The period of time comprising T time instants is called an epoch, so that each adjustable parameter is updated once every epoch. The update window size T, the learning rate α and the momentum constant μ, are three parameters that has an important role in the performance of the algorithm. If the learning rate is made too large, the algorithm becomes unstable, and if the learning rate is set too small, the algorithm takes a long time to converge. IV. ARTIFICIAL NEURAL NETWORK TRAINING A multi-layer feedforward artificial neural network is proposed to approximate the wind speed. This neural network has three hidden layers, the first hidden layer has 7 neurons, the second has 9 neurons and the third hidden layer has 15 neurons. The activation functions used in the three hidden layers are tansigmoid functions. The output layer has one neuron and the activation function is a purelin function. The inputs to the neural network are the turbine power P m,the wind turbine rotational speed w, and the blade pitch angle β, and the output is the estimated wind speed ˆv. The training data for the neural network P m, w, andβ are selected covering the entire operate range of the wind turbine generator.the network weights are adjusted such that the network output error is minimized. The technique used to train the network is the backpropagation with momentum algorithm [16]. 1386

5 The parameters of the neural network training algorithm was selected as follows: a learning rate of α =0.25, a momentum gain of μ =0.35 and an epoch of T =5time instants. Figure 2 shows the learning curve of the artificial neural network. In this figure it is represented the average squared error versus number of training epochs, which represent a set of training patterns Performance is , Goal is 0 Estimated and real wind speed (m/s) v v^ Average squared error Fig. 3. Real and estimated wind speed (m/s) Epochs Fig. 2. Learning curve Once the neural network was well trained, the wind speed can be obtained from the network output, using the network inputs P m, w, andβ. V. SIMULATION RESULTS In this section we will study the speed regulation performance of the wind turbine using the proposed neural network based wind speed estimator. In this example A 9-MW wind farm consisting of six 1.5 MW wind turbines connected to a 25-kV distribution system exports power to a 120-kV grid through a 30-km, 25-kV feeder. A 2300V, 2-MVA plant consisting of a motor load (1.68 MW induction motor at 0.93 PF) and of a 200-kW resistive load is connected on the same feeder at this 25-kV bus. Wind turbines use a doubly-fed induction generator (DFIG) consisting of a wound rotor induction generator and an AC/DC/AC IGBT-based PWM converter. The stator winding is connected directly to the 60 Hz grid while the rotor is fed at variable frequency through the AC/DC/AC converter. The DFIG technology allows extracting maximum energy from the wind for low wind speeds by optimizing the turbine speed, while minimizing mechanical stresses on the turbine during gusts of wind. The optimum turbine speed producing maximum mechanical energy for a given wind speed is proportional to the wind speed. In this example the wind speed in estimated using the proposed ANN. Figure 1 shows the real (thin line) and the estimated (thick line) wind speed, in this figure it can be observed that the ANN estimates the wind speed accurately, and that the control Wind turbine rotor speed (p.u) Fig. 4. Turbine rotor speed (pu) system of the wind turbine performs well using the wind speed estimated by the ANN. Figure 2 shows the wind turbine rotor that is expressed in per unit value (pu) of the generator synchronous speed. Figure 3 shows the active power (solid line) and reactive power (dashed line) generated by the windturbines, and Figure 4 shows the pitch angle of the blades that should be controlled in order to limit the mechanical power inside the values that the wind turbine can convert into electrical power. VI. CONCLUSION A new Neural Network wind speed estimator for variable speed wind turbine generator has been presented. The proposed estimator scheme employs the field oriented control theory in order to simplify the dynamic equations of the doubly fed induction generators. The proposed design avoids to employ anemometers to measure wind speed in order to track the desired optimal shaft speed. It should be noted that these mechanical sensors increase the cost and reduce the reliability of the wind turbine generator system. In the recent research works it can be found some maximum power tracking controls schemes, based on direct or indirect 1387

6 Active and reactive power (MW) Pitch angle (deg) Fig. 5. Active and reactive power 0 Fig. 6. Pitch Angle (Deg) wind speed estimation or prediction. However, these senseless control algorithms proposed in this works presents some drawbacks that can be solved by the Neural Network based estimator schemes like: 1) requiring significant memory space, 2) requiring complex and time-consuming calculations, and 3) not accurate for realtime control. These drawbacks reduce WTG system performance. This paper has proposed a wind speed estimation based sensorless output maximum control for variable-speed WTG systems. A specific design of the proposed control has been presented for a wind turbine driving a DFIG. A multilayer feedforward network and the so-called backpropagation with momentum algorithm which is a gradient descent algorithm of the performance function. The optimal DFIG rotor speed command is then determined from the estimated wind speed in order to achieve the maximum wind power extraction. Simulation studies have been carried out on a 9-MW wind farm consisting of six 1.5 MW wind turbines to verify the proposed sensorless control system. Results have shown that the wind speed was accurately estimated under both normal and transient operating conditions. The resulting WTG system P m Q delivered maximum electrical power to the grid with high efficiency and high reliability without mechanical anemometers. In addition, the proposed algorithm can be applied to other WTG systems. ACKNOWLEDGMENT The authors are very grateful to the Basque Government by the support of this work through the project S-PE09UN12 and to the UPV/EHU by its support through project GUI07/08. REFERENCES [1] S. BHOWMIK, R. SPEE, AND J. H. R. ENSLIN Performance optimization for doubly fed wind power generation systems. IEEE Trans. Ind. Appl., vol. 35, no. 4, pp [2] EZIO S., CLAUDIO C. 1998,.Exploitation of wind as an energy source to meet the worlds electricity demand Wind Eng, 76, [3] FUNG KT, SCHEFFLER RL, STOLPE J. 1981, Wind energya utility perspective. IEEE Trans Power Appar System, 100, [4] HAGAN M.T., DEMUTH H.B. AND BEALE M.H., 1996, Neural Network Design, Boston, MA: PWS Publishing. [5] HAYKIN S., 1994, Neural Networks, Macmillan, New York. [6] JOSELIN HERBERTA G.M., INIYANB S., SREEVALSANC B.E., RA- JAPANDIAN S. 2007, A review of wind energy technologies. Renewable and Sustainable Energy Reviews, 11, [7] YAZHOU LEI, ALAN MULLANE,GORDON LIGHTBODY, AND ROBERT YACAMINI 2006, Modeling of the Wind Turbine With a Doubly Fed Induction Generator for Grid Integration Studies, IEEE Trans. on Energy Conversion, vol. 21, no. 1, pp [8] H. LI, K. L. SHI, AND P. G. MCLAREN, 2005 Neural-network-based sensorless maximum wind energy capture with compensated power coefficient, IEEE Tran. Ind. Appl., vol. 41, no. 6, pp [9] M.V.A.NUNES, J.A.P.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, pp , Dec [10] OMIDVAR O., ELLIOTT D.L. 1997, Neural Systems for Control, Academic Press, New York. [11] R. PENA, J. C. CLARE, AND G. M. ASHER 1996 Doubly fed induction generator using back-to-back PWM converters and its application to variablespeed wind-energy generation Proc. Inst. Elect. Eng., vol. 143, no. 3, pp [12] WEI QIAO, WEI ZHOU, JOS M. ALLER, AND RONALD G. HARLEY 2008, Wind Speed Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a DFIG IEEE Trans. on Power Electronics, vol. 23, no. 3, pp [13] WEI QIAO, WEI ZHOU, JOS M. ALLER, AND RONALD G. HARLEY, Wind Speed Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a DFIG, IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 23, NO. 3, MAY [14] Y.D. SONG, B. DHINAKARAN,X.Y. BAO, 2000, Variable speed control of wind turbines using nonlinear and adaptive algorithms, Journal of Wind Engineering and Industrial Aerodynamics., 85, pp [15] K. TAN AND S. ISLAM, Optimal control strategies in energy conversion of PMSG wind turbine system without mechanical sensors. IEEE Trans. Energy Conversion, vol. 19, no. 2, pp [16] Hagan M.T., Demuth H.B. and Beale M.H., 1996, Neural Network Design, Boston, MA: PWS Publishing. [17] SALLE S.A., REARDON D., GRIMBLE M.J., 1990, AReview of wind turbine control. Int. J. Control, 52, [18] WORLD WIND ENERGY ASSOCIATION 2009 World Wind Energy Report 2008 WWEA Head Office. Bonn, Germany. 1388