A wind turbine system is operated such that the points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind turbine at an optimal rotor speed for a particular wind speed. A Maximum Power Point Tracking (MPPT) controller is used for this purpose. In fixed-pitch variable-speed wind turbines, wind-rotor parameters are fixed and the restoring torque of the generator needs to be adjusted to maintain optimum rotor speed at a particular wind speed for optimum power output. In turbulent wind environment, control of variable-speed fixed-pitch wind turbine systems to continuously operate at the maximum power points becomes difficult due to fluctuation of wind speeds. In this paper, wind speed forecasting techniques will be considered for predictive optimum control system of wind turbines

Jovanovic, Milutin

Lung, P. S.

Narayana, Mahinsasa

Putrus, Ghanim

English

Northumbria Research Link

Predictive Control of Wind Turbines by ConsideringWind Speed Forecasting TechniquesMahinsasa NarayanaNorthumbria Universitymahinsasa.narayana@unn.ac.ukGhanim PutrusNorthumbria Universityghanim.putrus@unn.ac.ukMilutin JovanovicNorthumbria Universitymilutin.jovanovic@unn.ac.ukPak Sing LeungNorthumbria Universityps.leung@unn.ac.ukAbstract- Fixed speed wind turbines have low efficiency ascompared to variable-speed, fixed-pitch wind turbines. Thelatter are required to optimize power output performancewithout the aerodynamic controls. A wind turbine system isoperated such that the points of wind rotor curve and electricalgenerator curve coincide. In order to obtain maximum poweroutput of a wind turbine generator system, it is necessary todrive the wind rotor at an optimal rotor speed for a particularwind speed. A Maximum Power Point Tracking (MPPT)controller is used for this purpose. In fixed-pitch variable-speedwind turbines, wind-rotor parameters are fixed and therestoring torque of the generator needs to be adjusted tomaintain optimum rotor speed at a particular wind speed foroptimum power output. In turbulent wind environment, controlof variable-speed fixed-pitch wind turbine systems tocontinuously operate at the maximum power points becomesdifficult due to fluctuation of wind speeds. Therefore, a specialemphasis is given to operating at maximum aerodynamic powerpoints of the wind rotor. In this study, wind speed forecastingtechniques are considered for predictive optimum controlsystem of wind turbines to reduce response time of the MPPTcontroller.Index Terms-- Maximum power point, Predictive control, Windspeed forecasting, and Wind turbineI. INTRODUCTIONWind speeds continuously varies and although wind rotor isrequired to drive at an optimal rotor speed for a particularwind speed, wind rotor speed can not be instantaneouslychanged. Therefore, the response of the wind rotor to windspeed variation affects the performance of the system. Windspeed time-series data typically exhibit autocorrelation, whichcan be defined as the degree of dependence on precedingvalues[1]. Autocorrelated time series models are usually usedfor wind speed prediction. In an autocorrelated wind speed-time series, the value of wind speed in any one time step isstrongly influenced by the values in previous time steps.Therefore, in this study wind speed prediction techniques areapplied to improve the response of wind rotor speed variationand energy capture.For optimum operation of wind turbines, if the wind speed isvaried from V1 to V2, the wind rotor rotational speed shouldbe changed from 1 to 2, as shown in Figure 1. In systemsthat employ wind speed sensors, the wind sensor provides theturbine rotational speed reference to the MPPT controlleraccording to the wind rotor characteristics. This reference iscompared with the actual rotational speed of the rotor. Windspeed, turbine rotational speed and wind rotor & generatorcharacteristics are the main factors that determine theoptimum operating points. Rotational speed of the systemcannot be instantly varied due to the wind rotor momentum ofinertia. Therefore, it is difficult to track optimal rotationalspeeds with wind speed variations. Response time of thecontroller depends on the turbulence dynamics and affects theperformance of the system.II. WIND SPEED FORECASTINGTime series prediction takes an existing series of data andforecasts the future values. Linear statistical models arecommonly used for time series prediction [2, 3]. The use ofNeural-networks is also a promising technology inforecasting and can be used to predict time series wind data[4-9]. Neural networks can be classified into dynamic andstatic categories. Static (feed-forward) networks have nofeedback elements and contain no delays; the output iscalculated directly from the input through feed-forwardconnections. In dynamic networks, the output depends notonly on the current input to the network, but also on theprevious inputs, outputs, or states of the network. Dynamicnetworks can also be divided into two categories: those thathave only feed-forward connections, and those that havefeedback, or recurrent connections. Dynamic neural networkmethod is more suitable for time series forecasting as it canbe trained to learn sequential or time-varying pattern [10, 11].For this study, a neural network incorporated with a tappeddelay line with delay from 1 to 5 and five neurons in hiddenlayer is used for wind speed prediction. This is a straightforward dynamic network, which consist of a feed-forwardnetwork trained by back-propagation with tapped delay line atan input. This is part of a dynamic network, called focusedV1V2134Optimum lineRotational speedPower21Wind rotorcurvesFig. 1. Function of MPPT mechanism of a wind energy conversion systemnetworks, in which the dynamics appear only at the inputlayer of a static multilayer feed-forward network[11]. This iscalled the Focused Time-Delay Neural Network (FTDNN).Levenberg-Marquardt algorithm was used to train thisnetwork[12]. Network architecture is shown in Figure 2. Thecharacteristics of the neural network used in this study are thefollowings;Number of Input Delays: 5Number of hidden layer: 1Number of neurons in hidden layer: 5Number of iterations for training: 100III. SYSTEM SIMULATIONMATLAB neural network toolbox was used to simulate thefocused time-delay neural network and trained incrementally.Measured wind data with 10 s sample time in the constrictedsite at Ekala, Sri Lanka was used to investigate the effectiveof neural networks for wind speed forecasting in turbulentwind condition. Dynamic neural network method was used topredict 10 second ahead wind speed value. Predicted andmeasured wind data are presented in Figure 3.In order to measure the accuracy of prediction, the root meansquare error (RMSE) was used.Root mean square error (RMSE)=  niipi vvn 121 (1)=0.6768where n is the total number of data points (135), vi, are actualvalues of wind speeds, vip are the predicted values for vi.According to these results, focused time-delay neural networkmethod is suitable for one time step ahead wind speedpredictions. Zhang et al [10] have described that NeuralNetworks (NNs) perform better than linear statistical models,when the data is highly variable. It is not expected that NNsdo better than linear models for linear relationship [13]. NNsare superior than linear statistical models, when wind speedsare predicted at a turbulent wind site where manydisturbances exist. Normally, small wind turbines areinstalled in more constricted places, where the wind speedsare more turbulent. However, the computational complexityof linear statistical models is better than that of neuralnetwork[5]. Most important thing is NNs are independent ofany mathematical model and adapt themselves to any kind ofdata set [14].IV. PREDICTIVE CONTROL OF WIND TURBINESA. Response time of MPPT mechanismResponse time of the controller depends on the turbulencedynamics and this effects to performance of the system[2].This study is intended to reduce response time of the MPPTcontroller by defining constrains by wind speed forecasting.For quick response to speed variation of the wind rotor,maintaining a maximum possible rate of change of wind rotorrotational speed is important. The restoring torque of agenerator is controllable.When wind speed is changed, variation of rotational speed ofthe system can be derived as follows;JTITvkTdtd fGea )()(),(   (2)Where-Rotational speed, v-wind speed, J– Momentum of inertia ofall rotating parts, ),( vTa - Aerodynamic torque by windrotor side, )( Ge IT -Restoring torque of generator(controllable variable), )(fT -Torque due to friction lossesof generator & gear box (function of rotational speed), k-Speed ratio of the gear box.Therefore, ideal response time of MPPT controller is;21)()(),(0dTITvkTJdttfGeaT(3)a- bias vectorW1,2W1,3W1,4W1,5TransferfunctionVt+1V(t-1)V(t-2)V(t-3)Z-1Z-1Z-1Z-1V(t)W1,1V(t-4)W2,1W2,2W2,3W2,4W2,5a- bias vectorTransferfunctionFig. 2. The structure of focused time-delay neural network012345670 200 400 600 800 1000 1200 1400Time (sec)Windspeed(m/s)MeasuredPrediction_NNFig. 3. NN predicted and measured wind data at Ekala, Sri LankaB. Improved controller with wind speed predictionIn this study, ideal control criteria were considered to achieveoptimum aerodynamic performance of the wind rotor(optimum tip-speed ratio). Predicted wind speed was used tocontrol the rotational speed of the wind rotor. Control criteriaare described in Table I and control diagram of the system ispresented in the Figure 5.Based on the wind turbine aerodynamic behaviour, theturbine catches only a part of the kinetic energy contained inthe wind[15]; that is:pra CvRP .....21 32 (4)where Pa is the captured power by the rotor, Rr is the radiusof the rotor (Rr =1.105m for this study),  is the air densityand v is the speed of the incident wind.The proportion of the useful power is defined by the powercoefficient Cp, which depends on the tip speed ratio ().The rotor aerodynamic characteristics are represented by theCp -  relationship. Wind-rotor characteristics (shown inFigure 4) were used for this simulation study. Momentum ofinertia of the system is 9.77kg.m2 and optimum tip-speedratio of the wind rotor is 6.3.Using equation (4), the aerodynamic torque (Ta) of a windrotor can be obtained as follows:praCvRT .....21 23 wherevRr.  (5)TABLE ICONTROL CRITERIAPerformance of the predictive controller was compared with aconventional controller which is operated without predictor.These two systems were simulated (in MATLAB) byconsidering ideal control criteria to obtain optimumaerodynamic performances of the wind rotor. In this study,the maximum reinstated power by the generator is 500Wwhich is typical for small scale wind turbines. Comparisonperformances are shown in Figure 6. Summary of energyextraction for 1350 s period is given in the Table II.TABLE IIENERGY EXTRACTIONWith wind speedpredictionWithout windspeed predictionAvailable energy 44.0218kJ 44.0218kJExtracted energy 34.9705kJ 32.9018kJActual energyextraction %79.43% 74.73%Available energy 3max5.0 vCA p  With wind speed predictiontechniquesWithout wind speed predictiontechniquesControl criteria:dtd is adjusted to achievepredicted optimum valueactualpredicted  Control criteria:dtd is adjusted to achievemeasured optimum valueactualoptimum  0510152025303540450 200 400 600 800 1000 1200 1400Time (s)Rotationalspeed(rad/s)OptimumActual with predictionActual without predictionFig. 6. Comparison performancePredictorWindspeedWind turbine speedControllerWind turbineSystemOutputpowerdemand-+Turbine speedreference(Lookuptable)rRv Fig. 5. Predictive control schemer with wind speed prediction0.000.100.200.300.400.500 1 2 3 4 5 6 7 8 9 10 11 12Tip-speed ratioCp0.000.020.040.060.08CTCpCTFig. 4. Wind rotor characteristicsactualoptimumpredictt t+1optimuactualt t+1V. RESULTS AND DISCUSSIONThis paper presents a comparison study of predictive MPPTcontrol by using wind speed forecasting. Control criteria weredeveloped by considering only rotor performance andsimulations were performed with ideal MPPT control criteriafor measured wind data. Performance of the predictive MPPTcontrol was compared with conventional MPPT controlsystem, which operates without predictor. The resultsobtained show that predictive control system improves theresponse time of the MPPT controller. The predictivecontroller performs well in turbulent wind condition andoptimises the control loop in real time. The proposedcontroller provides a good means to maximize the energycapture.REFERENCES[1] A. C. Brett and S. E. 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Halawani, "A neuralnetworks approach for wind speed prediction," Renewable Energy,vol. 13, pp. 345-354, 1998.[8] M. Bilgili, B. Shain, and A. Yasar, "Application of artificial neuralnetwork for the wind speed prediction of target station using referencestation data," Renewable Energy, vol. 32, pp. 2350-2360, 2007.[9] H. Li and R. Kozma, "A dynamic neural network method for timeseries prediction using the KIII model," in International JointConference on Neural Networks, Portland, 2003, pp. 14-19.[10] G. Zhag, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificialneural network: the state of the art," International journal ofForecasting, vol. 14, pp. 35-62, 1998.[11] D. B. Sandy and J. K. Ord, "Automatic neural network modelling forunivariate time series," International journal of Forecasting, vol. 16,pp. 509-515, 2000.[12] D. W. 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A dynamic neural network method for time series prediction using the KIII model,&quot;

A neural networks approach for wind speed prediction,&quot;

A new strategy for wind speed forecasting using artificial intelligent methods,&quot;

An Algorithm for Least-Squares Estimation of Nonlinear Parameters,&quot;

Application of artificial neural network for the wind speed prediction of target station using reference station data,&quot;

Automatic neural network modelling for univariate time series,&quot;

Forecasting wind speed with neural networks,&quot;

Forecasting with artificial neural network: the state of the art,&quot;

Locally recurrent neural network for wind speed prediction using spatial correlation,&quot; Information Science,

Predictive control of wind turbines in small power systems at high turbulent wind speeds,&quot;

The autocorrelation of hourly wind speed observations,&quot;

Time series analysis forecasting and control, 3rd edition ed.:

Wind Energy Handbook:

Wind speed forecasting for power system operational planning,&quot; in 8th international conference on probabilistic methods applied to power system Iowa state University,

Wind speed forecasting in the south cost of Oaxaca, Maxico,&quot;

Predictive control of wind turbines by considering wind speed forecasting techniques

Predictive control of wind turbines by considering wind speed forecasting techniques

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