prediction, operations, and condition monitoring in wind energy
TRANSCRIPT
lable at ScienceDirect
Energy 60 (2013) 1e12
Contents lists avai
Energy
journal homepage: www.elsevier .com/locate/energy
Review
Prediction, operations, and condition monitoring in wind energy
Andrew Kusiak a,*, Zijun Zhang b, Anoop Verma c
aDepartment of Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242e1527, USAbDepartment of Systems Engineering and Engineering Management, P6600, 6/F, Academic 1, City University of Hong Kong,Hong Kong Special Administrative RegioncDepartment of Mechanical and Aerospace Engineering, University at Buffalo, NY 14260, USA
a r t i c l e i n f o
Article history:Received 18 February 2013Received in revised form27 June 2013Accepted 24 July 2013Available online 23 August 2013
Keywords:Wind energyWind speed predictionWind turbine controlCondition monitoring and fault detection
* Corresponding author.E-mail addresses: [email protected] (A. K
(Z. Zhang), [email protected] (A. Verma).
0360-5442/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.energy.2013.07.051
a b s t r a c t
Recent developments in wind energy research including wind speed prediction, wind turbine control,operations of hybrid power systems, as well as condition monitoring and fault detection are surveyed.Approaches based on statistics, physics, and data mining for wind speed prediction at different timescales are reviewed. Comparative analysis of prediction results reported in the literature is presented.Studies of classical and intelligent control of wind turbines involving different objectives and strategiesare reported. Models for planning operations of different hybrid power systems including wind gener-ation for various objectives are addressed. Methodologies for condition monitoring and fault detectionare discussed. Future research directions in wind energy are proposed.
� 2013 Elsevier Ltd. All rights reserved.
1. Introduction
A rapid expansion of wind energy [1,2] has led to newchallengesin turbine control, plant operations, production planning, conditionmonitoring, and maintenance. Advances in research over the pastyears have provided numerous solutions to different problems. Theresearch has focused on variability of wind speed, increase of powergeneration efficiency, and reduction of the generation cost. Theneed to review and organize the generated knowledge is apparent.Greater understanding of the developed models and techniquescould positively impact future of wind energy research.
A number of review papers inwind energy have been published.Weisser and Garcia [3] surveyed wind-diesel hybrid systems anddiscussed their challenges. Ackermann and Soder [4] presentedhistorical developments of wind energy technology including is-sues in wind turbines and wind projects. Joselin Herbert et al. [5]provided a survey paper covers wind resource assessment, siteselection, and aerodynamic models, including wake effects. Foleyet al. [6] addressed concepts and techniques applied to wind powerforecasting. De La Salle et al. [7] reviewed wind turbine controlsystems developed before 1990. Review of wind energy technology
usiak), [email protected]
All rights reserved.
deployed in different regions [8,9] can also be observed in theliterature.
In the previous studies, the survey focused more on a specificarea in wind industry while the description of a larger map of windenergy research is rare. This paper aims to survey a wider scope ofrecent progress in developing models, methods, and techniques inkey areas of wind energy such as wind speed prediction, windturbine control, operation of hybrid power systems, conditionmonitoring, and fault detection.
2. Wind speed prediction
Determining the power generated by wind turbines at futuretimes is important for unit commitment planning andmaintenancescheduling. Wind speed must be predicted to estimate wind powergeneration capacity. Prediction (forecasting) of wind speed at threetime scales, short-, medium-, and long-term, is discussed. Short-term prediction aims at estimating wind speed at time intervalssuch as 10-sec or 10-min [10e12]. Medium-term wind speed pre-diction studies usually focus on hourly predictions [13e15], andlong-term wind speed prediction involves days [16e18]. Short-term wind speed prediction is important to control of wind tur-bines [19]. Medium-term wind speed prediction supports unitcommitment planning [19]. Long-term wind speed prediction isused in determining generation mix and scheduled maintenance ofpower systems [19]. Various approaches to wind speed prediction
Nomenclature
v wind speed valuec autoregressive (AR) coefficientst the current timem the number of prediction stepsI the order index of the AR processN the largest order indexE the error (white noise)s the moving trend of wind speedq the moving average coefficientp the order of the autoregressive process
q the order of the moving average processP the generated powerr the air densityR the rotor radiusCp($) the power coefficientb the blade pitch anglel the tip speed ratiou the shaft speedxc a set of controllable variablesxnc a set of non-controllable variablesfA($) the data-driven model trained by algorithm A
A. Kusiak et al. / Energy 60 (2013) 1e122
at different time scales have been developed in the past two de-cades. In Section 2.1 wind speed prediction methods are surveyed.The existing wind speed prediction studies are summarized inSection 2.2.
2.1. Prediction methods
Wind speed prediction methods can be grouped into four cat-egories, statistical, physics-based, data mining, and hybridmethods.
2.1.1. Statistical methodsSeveral variations of the AR (autoregressive) method [20e22]
are used in wind speed prediction. Brown et al. [23] publishedone of the earliest studies of modeling and prediction of windspeed with the AR method. The main goal of the study in Ref. [23]was to recognize several basic features of wind speed, such asautocorrelation, non-Gaussian distribution and diurnal non-stationarity. The general AR model for predicting wind speed wasexpressed in (1).
vt ¼XN
i¼1
ct;ivt�i þ εt ; t ¼ N þ T;N þ 2T;.;N þmT (1)
To predict wind speed based on (1), the order of AR process, i,needs to be determined. In the case study of [23], AR with i ¼ 1and i ¼ 2 were compared in hourly wind speed prediction. Asreported in Refs. [23], AR with i ¼ 2 provided lower residualsthan AR with i ¼ 1. Yule-Walker recursion [24] was utilized toestimate the AR coefficients. The AR method was extended toimprove accuracy of wind speed prediction. Bossanyi [12] com-bined the AR method with Kalman filters to predict wind speed.The AR method without the error term, ε in (1), was consideredas a persistence method and compared with the approach pro-posed by Bossanyi where the Kalman filter recursively estimatedAR coefficients based on updated measurements of the windspeed.
Huang and Chalabi [25] modified the AR wind speed predictionmodel by adding s. The modified AR model is expressed as:
vt ¼XN
i¼1
ct;ivt�i þ st þ εt (2)
The Kalman filter was also utilized to estimate the AR co-efficients and the order of AR is set to 2 in Ref. [25]. An ARMA(autoregressive and moving average) model was introduced byKamal and Jafri [26] to predict wind speed in 1997. The ARMAwindspeed prediction model is formulated as (3).
vt �m ¼Xp
ct;iðvt�i�mÞþ εt þ q1ðεt�1þ q2ðεt�1ÞÞþ.þ qq�εt�q
�
i¼1
(3)
where q1ðεt�1 þ q2ðεt�1ÞÞ þ.þ qqðεt�qÞrepresents the movingaverage process. The autoregressive coefficients, ct,i, were esti-mated based on Yule-Walker recursion [24]. The order parameters,p and q, were determined based on plots of the estimated auto-correlation function and partial autocorrelation function. To esti-mate the moving average coefficients, a least squares fit wasutilized. The ARMA model was also extended to predict both windspeed and direction [19,27,28].
The presented AR and its extensions emphasized short- andmedium-term wind speed prediction. The short-term wind speedprediction was conducted based on wind speed data measured atsecond to minute intervals. The study discussed in Ref. [11] utilizedan AR-based model to performwind speed prediction based on thefiltered second data. The proposed model was able to capture thecurvature of the wind speed. In Ref. [12], a wind speed predictionsover 5-min or 10-min intervals was examined and the resultsindicated that the incorporation of AR and Kalman filter out-performed the persistent method. Studies [23,25e29] presentedmedium-term wind speed predictions based on hourly data.Application of AR-based models in forecasting wind speed hoursahead was discussed in Refs. [25] and [29]. The results concludedthat the forecasting accuracy was decayed due to the increase ofprediction time-horizon. However, periodically adjustment of themodel could be utilized to maintain the performance of the pro-posed models. In Refs. [23,25e29], the prediction model accuracywas demonstrated based on different statistic metrics. However,besides [29], the comparison between the proposed method andother methods was rarely observed. Therefore, the real effective-ness of the proposed methods was not explicit.
2.1.2. Physics-based methodsNWP (numerical weather prediction) models are used to predict
wind speed, temperature, pressure, and humidity. The commonlydiscussed NWPmodel for wind speed predictionwas themesoscalemodel. It was capable to offer accurate ranges of daily wind speed.However, it usually did not provide a point-estimation of the windspeed in an exact time window. Therefore, the NWP model wasincorporated with the statistical methods to produce more reliablewind speed predictions. Zhao et al. [30] utilized the wind speedoutput of the NWPmodel as the input to a Kalman filter to improvewind speed prediction. Khalid and Savkin [31] applied AR model topredict wind speed for one turbine based on the measuredwind speed data of nearbywind turbines andwind speed data fromthe NWP model. Bhaskar and Singh [32] introduced an adaptivewavelet neural networkmodel for wind power forecasting based on
A. Kusiak et al. / Energy 60 (2013) 1e12 3
wind speed data from a NWP. Salonen et al. [33] validated the low-level NWP wind forecast model against radar wind observations.
In the studies presented in Refs. [30e32], a two-layer structurewas employed to combine NWP and statistical models. The NWPmodels provided the wind speed prediction results and then thoseresults were treated as inputs of the statistical models to generatemore accurate wind speed estimations. Short-, medium-, and long-term wind speed prediction based on the physics-based methodwere reported. In Ref. [31], the combination of NWP and AR modelwas introduced to predict wind speed and direction based on 10-min data. The proposed model was compared with a persistentmethod and a grey predictor to prove its effectiveness. In Ref. [32],the NWP was incorporated with NN (neural network) model topredict hourly wind speed. A 30-h ahead forecasting was offered.The proposed method was validated by comparing with a persis-tentmethod and a new-referencemodel. The incorporation of NWPand NN model was also applied to do daily wind speed prediction[30]. The prediction results were compared with observations toshow the accuracy of the proposed model.
2.1.3. Data mining modelsBesides statistical and physics-based methods, data mining has
been frequently considered for wind speed prediction. Wind speedexhibits both linear and nonlinear characteristics. Statisticalmethods usually focus on capturing the linear portion of the windspeed data while the nonlinear part is overlooked. Data miningmethods model both linearity and nonlinearity of the wind speed.Data mining regression algorithms, such as tree based regressionalgorithms [34e37], k nearest neighbor [38], SVM (support vectormachine) regression [39,40], NN (neural network) [41e43], andensemble data mining algorithms [44], have been applied to windspeed prediction. Some of the most accuratemodels for wind speedprediction have been developed from NN and SVM algorithms.
The NN model was applied to short- and long-term wind speedprediction. In short-termwind speed prediction, Kusiak and Li [45]used wind speed measured at different turbine locations to predictthe wind speed measured at a target turbine every 10-s based on aneural network ensemble algorithm [45]. A procedure for selectingturbine locations across a wind farm was discussed. The Pearson’scorrelation coefficient was applied to evaluate the affinity of thewind speed measured at different locations. Locations with highaffinity wind speed were selected to predict wind speed at thetarget location. Kusiak and Zhang [10] compared various predictionmethods, including exponential smoothing, a tree based datamining algorithm, a neural network, and support vector machines,for 10 s ahead wind speed predictions. Multi-step wind speedpredictions for horizons up to 1 min were reported. The neuralnetwork algorithm in Ref. [10] provided better prediction resultsthan other algorithms. Kusiak et al. [46] discussed a virtual sensorto estimate wind speed at a target wind turbine with a malfunc-tioning anemometer based on 10-s data. Residual analysis wasperformed to compare the observed and estimatedwind speed. Theresults of residual analysis indicated whether the anemometer wasmalfunctioning. This method could validate the wind speed mea-surements or be applied to sensorless measurement. Barbounis andTheocharis [47] published a recurrent neural network model forwind speed prediction based on 1-min data in 2007. The accuracyof a data mining model may suffer from limited data. The recurrentneural network algorithm overcame this challenge by using an on-line learning mechanism based on a recursive prediction errorapproach. The multi-step ahead prediction results for horizonsfrom 15 min to 3 h were reported. The recurrent neural networkmodel performed better than the persistence model. The applica-tions of NN in long-term predictionwere generally discussed basedon daily and monthly data. Mohandes et al. [48] presented a three
layer feed-forward neural network model to predict wind speed. Aback propagation algorithm was used to train the neural networkmodel and minimize the least square error. Three activation func-tions, hard limiter, sigmoid, and linear saturation, were utilized.The monthly and daily wind speed prediction based on NN wasinvestigated. An autoregressive model was considered as a baselineto compare with the neural network model. The test resultsdemonstrated that the neural network model outperformed theautoregressive model for one-step ahead and multi-step aheadpredictions. Bilgili et al. [49] applied neural networks to predict themonthly meanwind speed at a target wind farm based on themeanof wind speeds measured from neighboring wind farms. Themonthly mean wind speed was calculated based on the hourlycollected wind speed data. Poitras and Cormier [50] compared aneural network algorithm with a PSO (particle swarm optimiza-tion) [51] based method for daily wind speed prediction over 36months. A second order polynomial equation was used to predictwind speed in the PSO based method. PSO was used to tune thecoefficients in the second order polynomial equation in order tominimize the mean square error.
SVMr (support vector machine regression) is also frequentlyused for wind speed prediction. The original SVM (support vectormachine) algorithm [52] was developed for two-group classifica-tion. Drucker et al. [53] proposed a modified version of the SupportVector Machine algorithm for regression. In Ref. [53], the label ofeach instance (data point) is a real number in a regression problemrather than classes in a classification problem. In the literature, theSVM algorithm was applied for medium- and long-term windspeed prediction. Salcedo-Sanz et al. [54] applied the SVMr algo-rithm to predict wind speed at hourly intervals. Heuristic searchalgorithms, the evolutionary strategy and particle swarm optimi-zation algorithm were utilized to estimate the parameters of SVMin Ref. [54]. Mohandes et al. [55] compared the SVMr and NN al-gorithms for daily wind speed prediction and reported that a well-tuned SVMr model performs better than the NN model. Ortiz-Garcia et al. [56] presented another study on predicting hourlywind speed by SVMr algorithm. An ensemble of different SVMrmodels was investigated in Ref. [56] to develop a wind speed pre-diction model.
Tree based regression algorithms and k nearest neighbors havealso been applied to wind speed prediction in Refs. [10,45]. Ac-cording to [10,45], the NN and SVM models performed better thanthe tree based regression algorithms and k nearest neighbors forwind speed prediction.
2.1.4. Hybrid methodsBesides statistical, physics-based, and data mining approaches,
an ensemble of models based on algorithms of the same or differenttypes have been used inwind speed prediction. Hybridmethods formedium- and long-term wind speed predictions have been pub-lished. Inmedium-termprediction,Monfared et al. [57] developed anew hybrid strategy forwind speed prediction based on Fuzzy Logicand Neural Networks. This integrated approach provided improvedprediction results while learning timewas reduced. Thewind speeddata was sampled in 30-min intervals. Salcedo-Sanz et al. [58]applied a number of NNs (neural networks) for hourly wind speedprediction. Liu et al. [59] presented a hybrid method combiningwavelets and time series analysis to predict hourly wind speed forsmall wind farms. The results published in Ref. [59] indicate that themean relative error of the proposed hybrid method in multi-stepprediction was smaller than the classical time series method andthe back propagation method. In long-term prediction, Guo et al.[60] proposed a seasonal auto-regression integrated movingaveragemodel and a least square support vector machinemodel formonthly wind speed prediction in the Hexi Corridor of China. In
A. Kusiak et al. / Energy 60 (2013) 1e124
Refs. [60], performance of four algorithms was studied: the singleauto-regression integrated moving average, the seasonal auto-regression integrated moving average, the least square supportvector machine and the combination of auto-regression integratedmoving average, and least square support vector machine. Bouzgouand Benoudjit [61] introduced a multiple architecture system con-sisting of an ensemble of multiple linear regression, multi-layerperceptron, radial basis function, and support vector machine, topredict wind speed. Three types of ensemble strategies, simpleaverage, weighted average and non-linear combination, wereinvestigated. Daily wind speed data were analyzed.
2.2. Summary and discussion
Accuracy of the models developed in wind energy application isevaluated using statistical metrics, including MSE (mean squareerror), MAE (mean absolute error), MAPE (mean absolute per-centage error), RMSE (root mean square error), NMSE (normalizedmean square error), SDAE (standard deviation of absolute error)and SDAPE (standard deviation of absolute percentage error).
The MAE measures the absolute difference between the pre-dicted and the observed value. MAPE depicts the percentage errorof the predicted value and 1eMAPE explicitly demonstratesthe accuracy of a prediction model. Table 1 summarizes theMAPE of the wind speed prediction models reported in Refs.[10,19,27,45,49,60].
In the existing literature, wind speed is measured by ane-mometers located at the back of the nacelle which did not fullyreflect the real wind speed. The laser wind sensor [62] designed forsensing wind speed and direction in the approaching free-streaminflow offers more meaningful wind measurements and could beutilized to develop more reliable wind speed prediction models.
3. Wind turbine control
Control of variable speed wind turbines is challenging. Usually,two parameters, the generator torque and blade pitch angle, areconsidered.
3.1. Classical control
Two main control objectives are frequently considered in con-trol of wind turbines: 1) load reduction; 2) maximum powergeneration.
Novak et al. [63] reported on control of the drive train system ofa variable speed wind turbine. The dynamics of the drive trainsystem was described by three sub-models, drive train dynamics,aerodynamics, and generator dynamics. The three sub-modelswere integrated as a model of the drive train system. Three linearand nonlinear controllers were proposed and compared based onthe model developed through physics to improve system stability.
Table 1MAPE of different wind speed prediction models.
Method Reference Short-term(second tominute)
Mid-term(Half-hourto hour)
Long-term(day tomonth)
Hybrid [60] NA NA 6.76%NN [49] NA NA 4.49%NN [45] 11.57% NA NANN [10] 5.92% NA NAGrey Predictor [19] NA 7.41% NAARMA [27] NA 3.16% NA
NA ¼ not applicable.
Bossanyi [64] reviewed techniques for reduction of the wind tur-bine load. Two control strategies were designed for different windspeed ranges: 1) when wind speed larger than the rated value, apitch controller was designed to adjust the pitch angle of eachblade to limit power generation; 2) whenwind speed between cut-in and rated wind speed, a generator torque and pitch combinedcontroller was designed to reduce loads. Settings of generator tor-que and blade pitch were generated based on a signal representingthe difference between the measured value and demanded value ofparameters, provided by a feedback loop. Camblong et al. [65]designed and analyzed a discrete LQG (Linear Quadratic Gaussian)controller which could benefits the reduction of drive train fatigueloads of a wind turbine and the primary frequency control of thegrid. The generator torque and the pitch angle were considered ascontrol variables. The developed controller was tested in a simu-lation based on Simulink. Kumar and Stol [66] applied a nonlinearfeedback linearization controller with an Extended Kalman Filter tocontrol wind turbines when wind speed was above the rated windspeed. A LQR (linear quadratic regulator) was compared with theproposed controller. Superior rotor speed regulation was observedto be provided by the proposed controller when wind speed wasclose to the rated wind speed. Arrigan et al. [67] studied control offlapwise vibrations of wind turbine blades by installing semi-activetuned mass dampers. A semi-active algorithm was proposed totune the mass dampers based on the signal frequency. Shen et al.[68] investigated the control of wind turbine aerodynamics andloads in the wind shear flow which would lead to unsteady bladeairloads and performance. The advanced lifting surface methodwith time marching free wake model was utilized to model theturbine blades. Individual pitch control was applied to reduce theflapwise fatigue damage in the wind turbine blades.
Studies on maximizing power captured by wind turbines arecommonly based on the model (see (4) and (5)).
P ¼ 12rpR2CPðl; bÞv3 (4)
l ¼ uRv
(5)
Based on (4), various controllers were investigated to maximizegenerated power. Thiringer and Linders [69] studied control of awind turbine with fixed pitch angle by varying rotor speed. A dead-beat control system was designed in Ref. [69]. A reference powervalue and a nonlinear control parameter were both considered tovary the rotor speed of a wind turbine with fixed pitch angle. Per-formance of wind turbines with fixed and variable rotor speed wascompared. The results indicated that variable rotor speed controlproduced more power. Bhowmik et al. [70] proposed a variablespeed controller to optimize power output of a wind turbinegenerator. The controller included a wind speed estimation basedmaximum power point tracker and a heuristic model basedmaximum efficiency point tracker. The power coefficient of thepower point tracker was modeled as an nth-order polynomial toderive the optimal tip speed ratio. Then, the desired angular ve-locity of the wind turbine could be determined from the obtainedoptimal tip speed ratio. Based on the optimal angular velocity, themaximum power output was calculated for the wind turbine con-trol. Munteanu et al. [71] applied a Linear-Quadratic-Gaussiancontroller to optimize wind power generation of a fixed pitchwind turbine. The wind speed in this research was measured by ananemometer. The simulation results reported in Ref. [71] did notinclude a comparison with other controllers. Further investigationshould be undertaken to prove the quality of the LQG controllerdiscussed in the research. Boukhezzar et al. [72] discussed
A. Kusiak et al. / Energy 60 (2013) 1e12 5
optimization of the turbine power coefficient by adjusting gener-ator torque. Nonlinear static and dynamic state feedback control-lers were proposed. As the wind speed measured by a turbineanemometer is not precise due to the anemometer position, it wasnot considered in control. An estimator was developed to providewind speed in two steps: 1) a Kalman filter was used to estimatethe aerodynamic torque of thewind turbine; 2) thewind speedwasderived from the estimated aerodynamic torque based on (4). Theproposed controller was compared with a baseline control strategybased on the FAST (fatigue, aerodynamics, structures, and turbu-lence) simulator. Boukhezzar and Siguerdidjane [73] presented anextended study of wind turbine control discussed in Ref. [72] and anew nonlinear controller was proposed. The proposed nonlinearcontroller included an inner control loop and an outer control loop.The outer control loop controlled the mechanical system and pro-vided reference inputs for the inner loop controlling the electricalsystem of the wind turbine. Nonlinear static state feedback line-arization with PI (proportional-integral) action and wind speedestimator provided the best power generation efficiency. Evangel-ista et al. [74] proposed a simple robust second order sliding modecontroller for optimizing power production of a variable speedwind turbine. The simple robust controller was intended to addresswind speed variability, the nonlinear nature of the turbine system,model uncertainties, and external disturbances. Power efficiencywas maximized by tracking a reference speed. Performance of thecontroller in Ref. [74] was not compared with a baseline controller.Vlad et al. [75] presented a low-power wind energy conversionsystem with a permanent magnet synchronous generator. Themaximum power point tracking control approach was applied. Linand Hong [76] applied a Wilcoxon radial basis function networkwith hill-climb searching maximum power point tracking strategyto maximize the generated power of a variable speed wind turbinewith permanent-magnet synchronous generator. A real timeadjustment of the rotational speed of the wind turbine was real-ized. Iyasere et al. [77] studied a robust nonlinear controller formaximizing wind power production by a variable speed wind tur-bine. The controller optimized rotor speed and blade pitch angle. InRefs. [69e77], maximization of wind power generation bydesigning various controllers based on (4) was discussed. Optimi-zation of the power output was translated to the optimization ofpower coefficient, Cp($). The blade pitch angle b was fixed at theoptimal value and discussions of power coefficient optimizationwere mainly conducted based on the optimal l. Controllers withvarious control logic were designed to track the optimal shaftspeed, u, in order to maintain l at its optimal value via (5). Windpower production was maximized by optimizing torque. However,blade pitch control was not adequately discussed in Refs. [69e77].
Wind turbine control has been studied to achieve objectivesother than load reduction and power optimization. In Ref. [78],control of a wind turbine system to meet the wind farm grid coderequirement was discussed. Minimizing the variation of wind tur-bine power output by pitch angle control was investigated [79].Adaptive control of a DFIG (double-fed induction generator) windturbine to actively estimate and compensate for the plant dynamicsand external disturbances in real timewas presented in Ref. [80]. InRef. [81], a pitch control methodwas investigated to operate a 3 kWwind turbine to achieve the expected performance measured bygenerated power and stability.
3.2. Intelligent control
Accurately modeling the nonlinear relationship in (4) is chal-lenging. In traditional control [70e73], the power coefficient Cp($)was approximated by a nth-order polynomial or described by a Cpvs l curve. However, the function (4) does not adequately reflect
wind turbine operations [82]. Some papers have suggested devel-oping controllers without Eq. (4). Skikos and Machias [83] pro-posed a fuzzy logic controller. Four variables, yaw error, gridvoltage, grid frequency and wind turbine temperature, wereconsidered as control variables. Chen et al. [84] discussed a sto-chastic approach for maximizing power of fixed-speed wind tur-bines. The wind turbine controllers in Refs. [83,84] were notvalidated in the Megawatt variable speed wind turbines deployedat commercial wind farms. Developing intelligent controllers forMegawatt wind turbines has also been investigated. Data-drivenapproaches were utilized to model the wind power generationprocess. The data were collected by condition monitoring systems,e.g., SCADA (supervisory control and data acquisition) systems,installed at wind farms. A condition monitoring system usuallymonitors numerous parameters and the measurements can reflectwind turbine dynamics.
Neural Networks have often been used in modeling wind tur-bines. Kelouwani and Agbossou [85] applied a neural networkapproach to identify the power coefficient of a wind turbine fromdata. Historical data of average wind speed, standard deviation ofthe wind speed, and power output were considered as input vari-ables to predict the future power coefficient. Although an accuratepower coefficient can improve accuracy of wind power estimation,prediction of the power output directly rather than via the powercoefficient mitigates the impact of the cubic wind speed term. Theaccuracy of power predicted by NN in Ref. [10] was better than thewind power estimated from (4) based on the predicted power co-efficient. Generally, the data-driven model of power generation isexpressed as:
P ¼ fAðxc; xncÞ (6)
Kusiak et al. [86] presented a control approach based on data-driven models and evolutionary computation. Blade pitch angleand yaw angle were considered as controllable variables in Ref.[86]. A Neural Network was shown to be the most accurate algo-rithm for modeling a wind turbine. An evolutionary algorithmwas employed to compute optimal settings of the blade pitchangle and the yaw angle. Based on the data-driven frameworkpresented in Refs. [86], more advanced control strategies wereinvestigated. Kusiak and Zheng [87] discussed application of thedata-driven framework to maximize power generation efficiencyand increasing power quality. The simulation results indicated thatperformance of the wind turbine could be improved with intelli-gent controller. Based on the similar framework, Kusiak et al. [88]investigated anticipatory control for wind power maximization.The proposed models provided the current and future optimalsettings of controllable parameters. An adaptive control strategywas introduced in Ref. [89] to maximize power output and mini-mize the generator torque. Based on the results, the wind turbinecould realize an adaptive switch between power maximization andpower variation minimization. In addition to power maximization,minimization of wind turbine vibration has been studied. In Refs.[90,91], power output was maximized and turbine vibrations wereminimized by using SCADA data. Comparison of intelligent controland classical control presented in Refs. [92] and [93] demonstratedadvantages of intelligent control over classical control.
3.3. Summary and discussion
To fully compare the two approaches, benchmark studies areneeded. Most controllers discussed in the literature have not beenvalidated and deployed in practice. Implementation of the pro-posed controllers in industry to fully demonstrate their effective-ness is necessary.
Table 2Common failures associated with wind turbine components.
No. Component Fault type
1 Control system Fastening, dirt, corrosion2 Drive train Leakages, corrosion3 Hydraulic system Corrosion, cracks4 Tower and foundation Corrosion, cracks5 Nacelle Corrosion, cracks6 Safety devices
(sensors and braking systems)Damage, wear
7 Rotor blade Surface damage, cracks,structural discontinuities
A. Kusiak et al. / Energy 60 (2013) 1e126
4. Operations of hybrid power systems
Due to the variability of wind speed, supplying stable poweroutput to the grid has become a challenge in wind farms. Hybridpower systems have become a potential solution for tackling thisissue. Studies of incorporating wind farms and other power gen-eration systems including solar panels, hydro systems, thermalplants, as well as battery storage systems in power generation havebeen gradually increased in past years [94e96]. The majority of thepresented studies investigated the economic power dispatchproblem for the hybrid power system [97]. Yao et al. [98] investi-gated wind turbines and dual battery systems as a means to stablepower output. In Refs. [98], one of the dual battery systems wascharged by wind turbines and another one discharged power to thegrid. The battery systems created a buffer to manage fluctuations inwind power output. Perez-Navarro et al. [99] studied a hybridbiomass-wind power plant producing stable power. The biomasssystem in Ref. [99] served as a backup power generation system fora wind park. Chen [100] combined a branch-and-bound and a dy-namic programming algorithm to schedule a wind-thermal hybridsystem. Garcia-Gonzalez [101] applied stochastic optimization tosolve the unit commitment problem of a hybrid wind and pumped-storage system. Wind speed and price of electricity were consid-ered random parameters. In Ref. [102], a stochastic model for awind-thermal hybrid systemwas proposed. A self-adaptive particleswarm optimization algorithm was adopted to solve the stochasticmodel to generate optimal schedules for the unit commitmentmodel.
Deployment of hybrid power systems to mitigate powertransmission issues, limiting emissions, transmission during full-load hours, power transmission congestions, and overvoltage inlow-load scenarios were discussed in Refs. [103e105]. The eco-nomic power dispatch model was extended by including con-straints and adjusting the objective function to accommodatedifferent goals.
1. Catastrophic blade failure2. Catastrophic hub failure3. Main bearing failure4. Main shaft failure5. Gearbox failure6. Shaft-gearbox coupling failure7. Generator failure8. Tower failure9. Foundation failure10. Metrological system failure11. Premature brake activation12. Electrical system failure
1. Cracks in blade2. Dirt/ice built up3. Hub spinng on4. Blade pitch fau5. Shaft misalignm6. Yaw fault7. Cable twist8. Error in wind spmeasurement
Category A Categ
Fig. 1. Main faults of wind turb
5. Condition monitoring and fault detection
The maintenance cost for wind turbines due to failures such asspalled bearings, fractured gears, and drive train failures is signif-icant. Condition monitoring and fault detection aim to preventfailures and reduce maintenance cost. This section reviews condi-tion monitoring and fault detection approaches used in windenergy.
5.1. Wind turbine components and fault sources
Due to the dynamic nature of wind, failures inwind turbines canoccur anywhere between the tower base and the nacelle [106]. Alist of typical faults associatedwith turbine components is shown inTable 2.
Depending upon the extent of damage, the faults associatedwith turbine components are divided into three categories. Cate-gory A represents the most severe faults which can lead to shut-down of the turbine. Condition monitoring is developed to preventsuch faults types and minimize their impact. Category B faults canpartially affect the ability of a wind turbine to produce power.Category C faults arise due to over speeding and therefore can beeasily controlled (see Fig. 1).
Rotor speed, torque, yaw, and pitch angle affect operationalcharacteristics of a turbine. Component temperature and lubrica-tion are responsible for the condition of turbine components. Windspeed, wind turbulence, and ground drag are external factorsaffecting the turbines. The impact of external factors such as windturbulence and ground drag can be minimized by choosingappropriate wind farm location and tower height. Conditionmonitoring is required to minimize the impact of other indicators.Pacot et al. [107] reviewed the key indicators such as turbine age,size and location and their impact on management of wind farms.In the next section, condition monitoring and performance moni-toring approaches are discussed.
5.2. Recent methods
5.2.1. Condition monitoring of wind turbinesCondition monitoring implies continuous oversight of the
operating conditions of the equipment. This leads to the generationof early warnings so that the chance of a system failure is mini-mized. It is also useful in identifying the root cause of the fault andthus facilitating effective maintenance. Key characteristics of con-dition monitoring include: early warning, problem identification,and continuous monitoring. Due to irregular loads caused by tur-bulent wind conditions, the fatigue cycle of turbine rotating
son blades
shaftltent
eed/direction
1. Controller failure2. Hydraulic system failure3. Mechanical brake failure4. Pitching system failure
ory B Category C
ines grouped by severity.
A. Kusiak et al. / Energy 60 (2013) 1e12 7
components is greater than other rotating machines [108]. Even forunavoidable faults, overall damage can be minimized by gener-ating early alarm signals. This can be achieved by installingmonitoring systems. Condition monitoring of wind turbine drivetrain faults can lead to significant savings [109,110]. Vibrationanalysis, strain measurement, lubrication analysis, thermo-graphics, and acoustic analysis are common condition monitoringapproaches.
(a)Vibration analysis: Vibration analysis is used to evaluate theperformance of non-stationary components and is used formonitoring bearings (gearbox bearings and generator bearing)of wind turbines. Rotational speed, noise and stress are commonanalysis parameters as rotational speed and stress are indicatorsof component damage. Components affected by failures producenew vibration frequencies which allow the identification of a
Fig. 2. One-to-many relationship of condition monitoring te
change of status. A gearbox is a typical example hence vibrationanalysis is natural in detection of gearbox failures [111]. Orsaghet al. [112] demonstrated the application of vibration analysis inCBM (condition-based maintenance) of a drive train. Theyillustrated the benefits of HUMS (health and usage monitoringsystem) technology (initially developed for helicopters) in CBM.(b)Lubrication analysis: Oil and lubrication are an integral part ofthe rotating components of wind turbines. Offline oil analysis isfrequently used, however online oil monitoring offers additionalbenefits [111]. Lubrication analysis falls under the umbrella term‘tribology’ aimed at analysis of the dynamics of operating com-ponents and their support structures. Oil properties such asviscosity, water content, particle count, and presence of addi-tives are commonly used to identify faults. Tandon and Pary[113] surveyed the literature on oil analysis and its applicationsto wind turbine condition monitoring. Condition monitoring
chniques and their applications to turbine components.
A. Kusiak et al. / Energy 60 (2013) 1e128
approaches such as thermographics, acoustic analysis, visualinspection, and performance monitoring are used frequently.
The one-to-many relationship between condition monitoringtechniques and turbine components is shown in Fig. 2. It is clearfrom Fig. 2 that some condition monitoring approaches are suit-able for certain wind turbine components, while performance
Fig. 3. One-to-many relationship of performance monitoring
monitoring approaches are applicable to most wind turbinecomponent.
5.2.1.1. Condition monitoring of rotors. Caselitz et al. [114] pre-sented methods to analyze rotor anomalies. The most commonrotor anomalies are aerodynamic asymmetry and yaw misalign-ment [114,115]. Spectral analysis of electrical power generated by
techniques and their applications to turbine components.
A. Kusiak et al. / Energy 60 (2013) 1e12 9
the turbine is shown to be appropriate to detect those anomalies.Online analysis of rotor faults resulting from fatigue of blade ma-terial is being pursued. Amati and Brusa [116] developed vibrationmonitoring models to analyze the condition of AMBs (active mag-netic bearings). They analyzed the impact of non-uniform air gapand slip speed on bearing behavior. Shekar and Prabhu [117]applied a transient response approach to condition monitoring ofa rotor. They considered the impact of crack depth and eccentricityon phase and acceleration. Watson and Xiang [118] used powersignals to detect generator rotor misalignment and bearing faultsusing FFT (fast Fourier transformation) and wavelet analysis.
5.2.1.2. Condition monitoring of the gearbox and bearings.Fault diagnosis of bearings is usually performed using envelopecurve analysis based on high frequency resonances. Other methodssuch as cepstrum analysis are also used. A network based CMS(condition monitoring system) technique for fault diagnosis of aturbine gearbox and a rotor was reported in Ref. [114]. Amplitudeand frequency demodulation of the current signal of an inductionmotor is widely used to determine gearbox faults. Garcia et al. [119]used an intelligent search technique to identify and diagnosegearbox faults. They validated their model on an operating windturbine and developed an optimized maintenance schedule.
5.2.1.3. Condition monitoring of blades. Jeffries et al. [120] identifieddefects in the blades of a small wind turbine by analyzing powerspectrum density at the generator terminals. A normalized bis-pectrum namely ‘bicoherence’ was used to monitor small physicalchanges in the turbine. Tsai et al. [121] used a continuous wavelettransform approach to detect blade damage. In general, it is verydifficult to monitor turbine blade damage using wind turbinegenerator terminals as most of the time they can be affected bylightning which is random in nature. To alleviate this issue, modernturbine blades are equipped with lightning protection systems[122]. To monitor blade performance, a lightning impact localiza-tion and classificationmethod was discussed by Kramer et al. [123].They reported the usage of fiber optic current sensors for detectionof damage caused by lightning. Computational intelligence wasapplied to monitoring wind turbine components such as tower,nacelle, and power train Ref. [124].
5.2.2. Performance monitoring of wind turbinesPerformance monitoring based on data mining and statistical
methods offers an alternative to condition monitoring for windturbines [125e128]. Condition monitoring approaches usuallyrequire sensors to be installed at specific locations of awind turbinewhile performance monitoring relies on historical operational datarecorded by the SCADA system. Performance monitoring ap-proaches allow for early fault warning and on-line/off-line moni-toring. Fig. 3 shows performance monitoring approaches applied tovarious wind turbine components.
5.2.2.1. Performance monitoring of turbine blades. Turbine bladesare frequently affected by faults. A two-class classification model offaults utilizing turbine SCADA data and fault log information wasdeveloped in Ref. [126]. Turbine parameters such as wind speed,rotor speed, and nacelle revolutionwere used to detect blade angleimplausibility faults. Qiu et al. [129] developed a monitoringapproach for turbine blade pitch faults. The authors included time-sequence and probability models in their analysis. Turbine faultlogs information was used in Ref. [127] to extract hidden faultpatterns.
5.2.2.2. Performance monitoring of turbine gearbox. Gearboxrelated issues are usually addressed in the frequency domain by
utilizing vibration data. Zhang et al. [125] utilized high frequencyvibration data from controlled gearbox testing to identify faults.They applied a FFT (fast Fourier transformation) and k-meansclustering algorithm to detect faults at high speed stage of gearbox.
5.2.2.3. Performance monitoring of turbine generators. Faults ofwind turbine generators have been analyzed using temperaturemeasurements. Zaher et al. [130] developed a neural networkmodel for fault detection using generator fan speed, nacelle tem-perature, power output, and generator cooling air temperature. Inanother approach, a random forest algorithm was applied in Ref.[128] to analyze generator brush faults.
5.2.2.4. Performance monitoring of wind farm. Research on perfor-mance monitoring of wind farms has been reported in the litera-ture. Performance monitoring of a wind farm can provide an earlywarning of wind turbine performance deviation. Power curve in-formation was used in Ref. [131] to monitor several wind turbineswith a data transformation approach based on 3rd and 4th ordermoments. Yan et al. [132] presented an inverse data transformationmethod to detect changes in wind turbine performance.
5.3. Challenges and opportunities
Condition and performance monitoring approaches have shownpotential in early fault detection. Condition monitoring approachesprovide more accurate results in a controlled environment; how-ever, their applicability in real environments showsweaknesses. Onthe other hand, performance monitoring based approaches need tobe refined to provide more accurate results.
Current advances in data acquisition systems support progressin online monitoring of wind turbines. Component conditionmonitoring and performance monitoring could be combined toprovide better outcomes.
6. Conclusion
Models and methods applied to wind speed prediction, windturbine control, condition monitoring and fault detection of windturbineswere surveyed in this paper. Research results inwind speedprediction were grouped according to modeling approaches basedon statistics, physics-based, data mining, and hybrid approaches.Statistics and datamining algorithmsweremore frequently utilizedfor short- and medium-termwind speed prediction. Physics-basedapproaches were usually considered in long-term predictions.Short-term wind speed prediction was more beneficial to windturbine control while medium-term and long-term wind speedpredictions were more meaningful to wind power productionplanning and wind turbine maintenance scheduling.
Research in wind turbine control was surveyed. Control of windturbines was investigated to achieve two main objectives, loadreduction and power output maximization. Power maximizationwas frequently accomplished with an aerodynamics-based model.The resulting controllers maximized the power coefficient byestimating the optimal tip speed ratio. Fixed pitch turbines wereconsidered in many power optimization studies. Intelligent controlwas offered as an alternative to classical control. Data-driven ap-proaches were utilized to develop wind power generation modelsand computational intelligence was applied to compute optimalcontrol settings. Comparative analysis of classical and intelligentcontrol on practical and benchmark cases was lacking in the in-vestigations. The main body of the wind turbine control researchfocused on wind turbines with induction generators located onland. Wind turbines with PMSG (permanent magnet synchronous
A. Kusiak et al. / Energy 60 (2013) 1e1210
generators) received less attention. More research in control ofoffshore wind turbines is needed.
Various approaches to condition and performancemonitoring ofwind turbine components were reviewed. The frequently discussedcomponents included rotors, gearbox, bearing, blades, and gener-ators. Vibration, lubrication, and acoustic signals were consideredas major indicators in condition monitoring and performanceresearch. Frequency analysis was utilized to identify faults andreasoning about root causes. Time domain analysis was performedto monitor condition of components. Recent research in wind tur-bine condition monitoring focusedmore on individual componentsthan the entire system. Research on models for monitoring windturbine systems is needed.
Acknowledgements
This research was supported by funds from the Iowa EnergyCenter Grant No. 07-01 and City University Project No. 7200314.
References
[1] Wang Z, Shi J, Zhao Y. Technology roadmaps: China wind energy develop-ment roadmap 2050. Paris, France: International Energy Agency; 2011.Available at: http://www.iea.org/publications/freepublications/publication/china_wind.pdf.
[2] U. S. Department of Energy. 20% wind energy by 2030: increasing windenergy’s contribution to U.S. electricity supply. NREL/SR-500-41869. Golden,Co: National Renewable Energy Laboratory; 2008. Available at:, http://www.nrel.gov/docs/fy08osti/41869.pdf.
[3] Weisser D, Garcia RS. Instantaneous wind energy penetration in isolatedelectricity grids: concepts and review. Renewable Energy 2005;30(8):1299e308.
[4] Ackermann T, Soder L. Wind energy technology and current status: a review.Renewable and Sustainable Energy Reviews 2000;4(4):315e74.
[5] Joselin Herbert GM, Iniyan S, Sreevalsan E, Rajapandian S. A review of windenergy technologies. Renewable and Sustainable Energy Reviews2007;11(6):1117e45.
[6] Foley AM, Leahy PG, Marvuglia A, McKeogh EJ. Current methods and ad-vances in forecasting of wind power generation. ReneCble Energy 2012;37(1):1e8.
[7] De La Salle SA, Reardon D, Leithead WE, Grimble MJ. Review of wind turbinecontrol. International Journal of Control 1990;52(6):1295e310.
[8] Himri Y, Boudghene Stambouli A, Draoui B, Himri S. Review of windenergy use in Algeria. Renewable and Sustainable Energy Reviews 2009;13(4):910e4.
[9] Hepbasli A, Ozgener O. A review on the development of wind energy inTurkey. Renewable and Sustainable Energy Reviews 2004;8(3):257e76.
[10] Kusiak A, Zhang Z. Short-horizon prediction of wind power: a data-drivenapproach. IEEE Transactions on Energy Conversion 2010;25(4):1112e22.
[11] Riahy GH, Abedi M. Short term wind speed forecasting for wind turbineapplications using linear prediction method. Renewable Energy 2008;33(1):35e41.
[12] Bossanyi EA. Short-term wind prediction using Kalman filters. Wind Engi-neering 1985;9(1):1e8.
[13] Liu H, Shi J, Erdem E. Prediction of wind speed time series using modifiedTaylor Kriging method. Energy 2010;35(12):4870e9.
[14] Song YD. A new approach for wind speed prediction. Wind Engineering2000;24(1):35e47.
[15] Hong Y, Chang H, Chiu C. Hour-ahead wind power and speed forecastingusing simultaneous perturbation stochastic approximation (SPSA) algorithmand neural network with fuzzy inputs. Energy 2010;35(9):3870e6.
[16] Bouzgou H, Benoudjit N. Multiple architecture system for wind speed pre-diction. Applied Energy 2011;88(7):2463e71.
[17] Guo Z, Zhao J, Zhang W, Wang J. A corrected hybrid approach for wind speedprediction in hexi corridor of China. Energy 2011;36(3):1668e79.
[18] Carro-Calvo L, Salcedo-Sanz S, Kirchner-Bossi N, Portilla-Figueras A, Prieto L,Garcia-Herrera R, et al. Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing.Energy 2011;36(3):1571e81.
[19] El-Fouly THM, El-Saadany EF, Salama MMA. One day ahead prediction ofwind speed and direction. IEEE Transactions on Energy Conversion2008;23(1):191e201.
[20] Jones RH. Maximum likelihood fitting of ARMA models to time series withmissing observations. Technometrics 1980;22(3):389e95.
[21] Gardner G, Harvey AC, Phillips GDA. Algorithm AS154: an algorithm for exactmaximum likelihood estimation of autoregressive-moving average modelsby means of Kalman filtering. Applied Statistics 1980;29(3):311e22.
[22] Brockwell PJ, Davis RA. Introduction to time series and forecasting. NewYork: Springer; 1996.
[23] Brown BG, Katz RW, Murphy AH. Time series models to simulate and fore-cast wind speed and wind power. Journal of Climate and Applied Meteo-rology 1984;23(8):1184e95.
[24] Box G, Jenkins GM, Reinsel G. Time series analysis: forecasting and control.3rd ed. Prentice Hall; 1994.
[25] Huang Z, Chalabi ZS. Use of time-series analysis to model and forecast windspeed. Journal of Wind Engineering and Industrial Aerodynamics1995;56(2e3):311e22.
[26] Kamal L, Jafri YZ. Time series models to simulate and forecast hourly aver-aged wind speed in Quetta, Pakistan. Solar Energy 1997;61(1):23e32.
[27] Erdem E, Shi J. ARMA based approaches for forecasting the tuple of windspeed and direction. Applied Energy 2011;88(4):1405e14.
[28] Jeon J, Taylor JW. Using conditional kernel density estimation for wind po-wer density forecasting. Journal of the American Statistical Association2012;107(497):66e79.
[29] Torres JT, Garcia A, De Blas M, De Francisco A. Forecast of hourly average windspeedwith ARMAmodels in Navarre (Spain). Solar Energy 2005;70(1):65e77.
[30] Zhao P, Wang J, Xia J, Dai Y, Sheng Y, Yue J. Performance evaluation andaccuracy enhancement of a day-ahead wind power forecasting system inChina. Renewable Energy 2012;43:234e41.
[31] Khalid M, Savkin AV. A method for short-term wind power prediction withmultiple observation points. IEEE Transactions on Power Systems 2012;27(2):579e86.
[32] Bhaskar K, Singh SN. AWNN-assisted wind power forecasting usingfeed-forward neural network. IEEE Transactions on Sustainable Energy 2012;3(2):306e15.
[33] Salonen K, Niemela S, Fortelius C. Application of radar wind observations forlow-level NWP wind forecast validation. Journal of Applied Meteorology andClimatology 2011;50(6):1362e71.
[34] Friedman JH. Stochastic gradient boosting. Computational Statistics and DataAnalysis 2002;38(4):367e78.
[35] Friedman JH. Greedy function approximation: a gradient boosting machine.Annals of Statistics 2001;29(5):1189e232.
[36] Breiman L. Random forests. Machine Learning 2001;45(1):5e32.[37] Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression
trees. Monterey, CA: Wadsworth & Brooks/Cole; 1984.[38] Shakhnarovish G, Darrell T, Indyk P. Nearest-neighbor methods in learning
and vision. Cambridge, MA: The MIT Press; 2005.[39] Schölkopf B, Burges CJC, Smola AJ. Advances in kernel methods: support
vector learning. Cambridge, MA: The MIT Press; 1999.[40] Steinwart I, Christmann A. Support vector machines. New York: Springer-
Verlag; 2008.[41] Siegelmann H, Sontag E. Analog computation via neural networks. Theo-
retical Computer Science 1994;131(2):331e60.[42] Liu GP. Nonlinear identification and control: a neural network approach.
London, UK: Springer; 2001.[43] Smith M. Neural networks for statistical modeling. New York: Van Nostrand
Reinhold; 1993.[44] Hansen LK, Salamon P. Neural network ensembles. IEEE Transactions on
Pattern Analysis and Machine Intelligence 1990;12(10):993e1001.[45] Kusiak A, Li W. Estimation of wind speed: a data-driven approach. Journal of
Wind Engineering and Industrial Aerodynamics 2010;98(10e11):559e67.[46] Kusiak A, Zheng HY, Zhang Z. A wind speed virtual sensor for wind turbines.
ASCE Journal of Energy Engineering 2011;137(2):60e9.[47] Barbounis TG, Theocharis JB. Locally recurrent neural networks for wind
speed prediction using spatial correlation. Information Sciences 2007;177(24):5775e97.
[48] Mohandes MA, Rehman S, Halawani TO. A neural networks approach forwind speed prediction. Renewable Energy 1998;13(3):345e54.
[49] Bilgili M, Sahin B, Yasar A. Application of artificial neural networks for thewind speed prediction of target station using reference stations data.Renewable Energy 2007;32(14):2350e60.
[50] Poitras G, Cormier G. Wind speed prediction for a target station using neuralnetworks and particle swarm optimization. Wind Engineering 2011;35(3):369e80.
[51] Kennedy J, Eberhart RC. Particle swarm optimization. In: IEEE internationalconference on neural networks 1995. p. 1942e8.
[52] Cortes C, Vapnik V. Support-vector networks. Machine Learning 1995;20(3):273e97.
[53] Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V. Support vectorregression machines. In: Advances in neural information processing systems,vol. 9; 1997. p. 155e61.
[54] Salcedo-Sanz S, Ortiz-Garcia EG, Perez-Bellido AM, Portilla-Figueras A,Prieto L. Short termwind speed prediction based on support vector regressionalgorithms. Expert Systems with Applications 2011;38(4):4052e7.
[55] Mohandes MA, Halawani TO, Rehman S, Hussain AA. Support vector ma-chines for wind speed prediction. Renewable Energy 2004;29(6):939e47.
[56] Ortiz-Garcia EG, Salcedo-Sanz S, Perez-Bellido AM, Gascon-Moreno J, Portilla-Figueras JA, Prieto L. Short-termwind speed prediction inwind farms based onbanks of support vector machines. Wind Energy 2011;14(2):193e207.
[57] Monfared M, Rastegar H, Kojabadi HM. A new strategy for wind speedforecasting using artificial intelligent methods. Renewable Energy 2009;34(3):845e8.
A. Kusiak et al. / Energy 60 (2013) 1e12 11
[58] Salcedo-Sanz S, Perez-Bellido AM, Ortiz-Garcia EG, Portilla-Figueras A,Prieto L, Correoso F. Accurate short-term wind speed prediction byexploiting diversity in input data using banks of artificial neural networks.Neurocomputing 2009;72(4e6):1336e41.
[59] Liu H, Tian H, Chen C, Li Y. A hybrid statistical method to predict wind speedand wind power. Renewable Energy 2010;35(8):1857e61.
[60] Guo Z, Zhao J, Zhang W, Wang J. A corrected hybrid approach for wind speedprediction in hexi corridor of China. Energy 2011;36(3):1668e79.
[61] Bouzgou H, Benoudjit N. Multiple architecture system for wind speed pre-diction. Applied Energy 2011;88(7):2463e71.
[62] Blue Scout, http://www.bluescout.com, accessed in 2012.[63] Novak P, Ekelund T, Jovik I, Schmidtbauer B. Modeling and control of
variable-speed wind-turbine drive-system dynamics. IEEE Control Systems1995;15(4):28e38.
[64] Bossanyi EA. Wind turbine control for load reduction. Wind Energy2003;6(3):229e44.
[65] Camblong H, Nourdine S, Vechiu I, Tapia G. Control of wind turbines forfatigue loads reduction and contribution to the grid primary frequencyregulation. Energy 2012;48(1):284e91.
[66] Kumar A, Stol K. Simulating feedback linearization control of wind turbinesusing high-order models. Wind Energy 2009;13(5):419e32.
[67] Arrigan J, Pakrashi V, Basu B, Nagarajaiah S. Control of flapwise vibrations inwind turbine blades using semi-active tuned mass dampers. StructuralControl and Health Monitoring 2010;18(8):840e51.
[68] Shen X, Zhu X, Du Z. Wind turbine aerodynamics and loads control in windshear flow. Energy 2011;36(3):1424e34.
[69] Thiringer T, Linders J. Control by variable rotor speed of a fixed-pitch windturbine operating in a wide speed range. IEEE Transactions on Energy Con-version 1993;8(3):520e6.
[70] Bhowmik S, Spee R, Enslin JHR. Performance optimization for doubly fedwind power generation systems. IEEE Transactions on Industry Applications1999;35(4):949e58.
[71] Munteanu I, Cutululis NA, Bratcu AI, Ceanga E. Optimization of variable speedwind power systems based on a LQG approach. Control Engineering Practice2005;13(7):903e12.
[72] Boukhezzar B, Siguerdidjane H, Maureen Hand M. Nonlinear control of vari-able speed wind turbines for generator torque limiting and power optimiza-tion. ASME Journal of Solar Energy Engineering 2006;128(4):516e30.
[73] Boukhezzar B, Siguerdidjane H. Nonlinear control with wind estimation of aDFIG variable speed wind turbine for power capture optimization. EnergyConversion and Management 2009;50(4):885e92.
[74] Evangelista C, Puleston P, Valenciaga F. A simple robust controller for powermaximization of a variable-speed wind turbine. International Journal ofEnergy Research 2010;34(10):924e32.
[75] Vlad C, Munteanu I, Bratcu AI, Ceanga E. Output power maximization of low-power wind energy conversion systems revisited: possible control solutions.EnergyCnversion and Management 2010;51(2):305e10.
[76] Lin W, Hong C. Intelligent approach to maximum power point trackingcontrol strategy for variable-speed wind turbine generator system. Energy2010;35(6):2440e7.
[77] Iyasere E, Salah MH, Dawson DM, Wagner JR, Tatlicioglu E. Robust nonlinearcontrol strategy to maximize energy capture in a variable speed wind tur-bine with an internal induction generator. Journal of Control Theory Appli-cations 2012;10(2):184e94.
[78] Muyeen SM, Takahashi R, Murata T, Tamura J. A variable speed wind turbinecontrol strategy to meet wind farm grid code requirements. IEEE Trans-actions on Power Systems 2010;25(1):331e40.
[79] Senjyu T, Sakamoto R, Urasaki N, Funabashi T, Fujita H, Sekine H. Outputpower leveling of wind turbine generator for all operating regions by pitchangle control. IEEE Transactions on Energy Conversion 2006;21(2):467e75.
[80] Song Z, Shi T, Xia C, Chen W. A novel adaptive control scheme for dynamicperformance improvement of DFIG-based wind turbines. Energy 2012;38(1):104e17.
[81] Nagai BM, Ameku K, Roy JN. Performance of a 3 kW wind turbine generatorwith variable pitch control system. Applied Energy 2009;86(9):1774e82.
[82] Kusiak A, Li W, Song Z. Dynamic control of wind turbines. Renewable Energy2010;35(2):456e63.
[83] Skikos GD, Machias AV. Design and development of fuzzy logic control stra-tegies for wind turbine generators. Wind Engineering 1995;19(5):235e48.
[84] Chen P, Siano P, Bak-Jensen B, Chen Z. Stochastic optimization of wind tur-bine power factor using stochastic model of wind power. IEEE Transactionson Sustainable Energy 2010;1(1):19e29.
[85] Kelouwani S, Agbossou K. Nonlinear model identification of wind turbine withaneural network. IEEETransactions onEnergyConversion2004;19(3):607e11.
[86] Kusiak A, Song Z, Zheng HY. Power optimization of wind turbines withdata mining and evolutionary computation. Renewable Energy 2010;35(3):695e702.
[87] Kusiak A, Zheng HY. Optimization of wind turbine energy and power factorwith an evolutionary computation algorithm. Energy 2010;35(3):1324e32.
[88] Kusiak A, Song Z, Zheng HY. Anticipatory control of wind turbines with data-driven predictive models. IEEE Transactions on Energy Conversion2009;24(3):766e74.
[89] Kusiak A, Zhang Z. Adaptive control of a wind turbine with data miningand swarm intelligence. IEEE Transactions on Sustainable Energy 2011;2(1):28e36.
[90] Kusiak A, Zhang Z, Li MY. Optimization of wind turbine performancewith data-drivenmodels. IEEE Transactions on Sustainable Energy 2010;1(2):66e76.
[91] Kusiak A, Zhang Z. Control of wind turbine power and vibration with a data-driven approach. Renewable Energy 2012;43(7):73e82.
[92] Soares O, Goncalves H, Martins A, Carvalho A. Nonlinear control of thedoubly-fed induction generator in wind power systems. Renewable Energy2010;35(8):1662e70.
[93] Kasiri H, Abadeh MS, Momeni HR. Optimal estimation and control of WECSvia a genetic neuro fuzzy approach. Energy 2012;40(1):438e44.
[94] Howlader A, Izumi Y, Uehara A, Urasaki N, Senjyu T, Yona A, et al. A minimalorder observer based frequency control strategy for an integrated wind-battery-diesel power system. Energy 2012;46(1):168e78.
[95] Niknam T, Golestaneh F, Malekpour A. Probabilistic energy and operationmanagement of a microgrid containing wind/photovoltaic/fuel cell genera-tion and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm. Energy 2012;43(1):427e37.
[96] Kaabeche A, Belhamel M, Ibtiouen R. Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system. Energy2011;36(2):1214e22.
[97] Fusco F, Nolan G, Ringwood J. Variability reduction through optimal com-bination of wind/wave resourcesean Irish case study. Energy 2010;35(1):314e25.
[98] Yao DL, Choi SS, Tseng KJ, Lie TT. Determination of short-term powerdispatch schedule for a wind farm incorporated with dual-batteryenergy storage scheme. IEEE Transactions on Sustainable Energy2012;3(1):74e84.
[99] Perez-Navarro A, Alfonso D, Alvarez C, Ibanez F, Sanchez C, Segura I. Hybridbiomass-wind power plant for reliable energy generation. Renewable Energy2010;35(7):1436e43.
[100] Chen C. Optimal wind-thermal generating unit commitment. IEEE Trans-actions on Energy Conversion 2008;23(1):273e80.
[101] Garcia-Gonzalez J, de la Muela RMR, Santos LM, Gonzalez AM. Stochasticjoint optimization of wind generation and pumped-storage units in anelectricity market. IEEE Transactions on Power Systems 2008;23(2):460e8.
[102] Pappala VS, Erlich I, Rohrig K, Dobschinski J. A stochastic model for theoptimal operation of a wind-thermal power system. IEEE Transactions onPower Systems 2009;24(2):940e50.
[103] Bahmani-Firouzi B, Farjah E, Azizipanah-Abarghooee R. An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimizationapproach for dynamic economic emission dispatch considering load andwind power uncertainties. Energy 2013;50:232e44.
[104] Kargarian A, Raoofat M. Stochastic reactive power market with volatility ofwind power considering voltage security. Energy 2011;36(5):2565e71.
[105] Billinton R, Wangdee W. Reliability-based transmission reinforcementplanning associated with large-scale wind farms. IEEE Transactions on PowerSystems 2007;22(1):34e41.
[106] Ashley F, Cipriano RJ, Breckenridge S, Briggs GA, Gross LE, Hinkson J, et al.Report from the Bethany Wind Turbine Study Committee. Available at:http://docs.wind-watch.org/bethany-windturbinestudycommittteereport.pdf; 2007.
[107] Pacot C, Hasting D, Baker N. Wind farm operation and maintenance man-agement. In: Proceedings of the power-Gen conference Asia, Ho Chi MinhCity, Vietnam 2003. p. 25e7.
[108] Chandler H, editor. Wind energydthe facts. European Wind Energy Asso-ciation; 2003. Available at: www.ewea.org06-05-02S.
[109] Wilkinson MR, Tavner PJ. Extracting condition monitoring information froma wind turbine drive train. In: Proceedings of Universities’ power engi-neering conference 2004. p. 591e4.
[110] Wilkinson MR, Tavner PJ. Condition monitoring of wind turbine drive trains.In: Proceedings of international conference on electrical machines 2006.p. 1e6.
[111] Mobley RK. An introduction to predictive maintenance. 2nd ed. Heinemann:Butterworth; 2006.
[112] Orsagh RF, Lee H, Watson M, Byington CS, Powers J. Advanced vibrationmonitoring for wind turbine health management. Impact Technologies;2011. Available at: http://www.impact-tek.com.
[113] Tandon N, Parey A. Condition monitoring of rotary machines. In: Conditionmonitoring and control for intelligent manufacturing. Springer series inManufacturing; 2006. p. 109e36.
[114] Caselitz P, Giebhardt J, Mevenkamp M. Application of condition monitoringsystems in wind energy convertors. In: Proceedings of the European windenergy conference, Dublin 1997. p. 1e4.
[115] Caselitz P, Giebhardt J, Mevenkamp M. Online fault detection and predictionin wind energy convertors. In: Proceedings of the European wind energyconference, Thessaloniki 1994. p. 623e7.
[116] Amati N, Brusa E. Condition monitoring of rotors on active magnetic bearings(AMB) fed by induction motors. In: Proceedings of IEEE/ASME advancedengineering mechatronics (AIM) 2001. p. 750e6.
[117] Shekar AS, Prabhu BS. Condition monitoring of cracked rotors throughtransient response. Mechanism and Machine Theory 1998;33(8):1167e75.
[118] Watson SJ, Xiang J. Real-time condition monitoring of offshore wind tur-bines. In: Proceedings of European wind energy conference 2006. Paper#BL-3.
A. Kusiak et al. / Energy 60 (2013) 1e1212
[119] Garcia MC, Sanz-Bobi MA, Del Pico J. SIMAP: intelligent system for predictivemaintenance application to the health condition monitoring of a wind tur-bine gearbox. Journal of Computers in Industry 2006;57(6):552e68.
[120] Jeffries WQ, Chambers JA, Infield DG. Experience with bi-coherence ofelectrical power for condition monitoring of wind turbine blades. IEEEProceedings on Vision Image & Signal Processing 1998;145:141e8.
[121] Tsai CS, Hsieh CT, Huang SJ. Enhancement of damage-detection of windturbine blades via CWT-based approaches. IEEE Transactions on EnergyConversion 2006;21(3):776e81.
[122] Cotton I, Jenkins N, Pandiaraj K. Lightning protection for wind turbine bladesand bearings. Wind Energy 2001;4(1):23e37.
[123] Kramer SGM, Leon FP, Appert B. Fiber optic sensor network for lightningimpact localization and classification in wind turbines. In: Proceedings ofIEEE International conference on multidimensional finance, insurance andinvestment 2006. p. 173e8.
[124] Ribrant J. Reliability performance and maintenanceda survey of failures inwind power systems. Master thesis. Stockholm, Sweden: KTH School ofElectrical Engineering; 2006.
[125] Zhang Z, Verma A, Kusiak A. Fault analysis of the wind turbine gearbox. IEEETransactions on Energy Conversion 2012;27(2):526e35.
[126] Kusiak A, Verma A. A data-driven approach for monitoring blade pitchfaults in wind turbines. IEEE Transactions on Sustainable Energy 2011;2(1):87e96.
[127] Kusiak A, Verma A. Prediction of status patterns of wind turbines: a data-mining approach. ASME Journal of Solar Energy Engineering 2011;133(1).011008-1e011008-10.
[128] Verma A, Kusiak A. Fault monitoring wind turbine generator brushes: a datamining approach. ASME Journal of Solar Energy Engineering 2012;134(2):021001-1e021001-9.
[129] Qiu Y, Richardson P, Feng Y, Tavner P, Erdos G, Viharos ZS. SCADA alarmanalysis for improving wind turbine reliability. In: European wind energyconference and exhibition, Brussels, Belgium 2011. p. 14e7.
[130] Zaher A, McArthur SDJ, Infield DG, Patel Y. Online wind turbine faultdetection through automated SCADA data analysis. Wind Energy 2009;12(6):574e93.
[131] Kusiak A, Verma A. Monitoring wind farms with performance curves. IEEETransactions on Sustainable Energy 2013;4(1):192e9.
[132] Yan Y, Osadciw LA, Benson G, White E. Inverse data transformation forchange detection in wind turbine diagnostics. In: Proceedings of Canadianconference on electrical and computer engineering 2009. p. 944e9.