Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction

Transcription

1 applied sciences Article Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase Load Reduction Hyungyu Kim, Kwansu Kim Insu Paek * Department of Advanced Mechanical Engineering, Kangwon National University, Chuncheon-si 24341, Gangwon-do, Korea; khg0104@kangwon.ac.kr (H.K.); kwansoo@kangwon.ac.kr (K.K.) * Correspondence: paek@kangwon.ac.kr; Tel.: Received: 2 August 2017; Accepted: 11 October 2017; Published: 16 October 2017 Abstract: A new farm algorithm that adjusts power output of most upstream turbine in a farm for power increase load reduction was developed in this study. The algorithm finds power comms to individual turbines to maximize total power output from farm when power comm from transmission system operator is larger than total available power from farm. To validate this farm algorithm, a relatively high fidelity farm simulation tool developed in previous study was modified to include a farm ler which consists of a speed estimator, a power comm calculator a simplified farm model. In addition, turbine ler in simulation tool was modified to include a demed power tracking algorithm. For a virtual farm three 5 MW turbines aligned, simulations were performed various ambient turbulent intensities, turbine spacing, frequencies. It was found from dynamic simulation using turbulent s that proposed farm algorithm can increase power output decrease tower load of most upstream turbine compared results conventional farm. Keywords: farm ; farm model; function minimization; power increase 1. Introduction Offshore farms using fixed substructures are mostly constructed in seawaters having a depth of less than 40 m, this often limits number of turbines that can be installed in a farm. The spacing between turbines commonly range from 7 RD to 11 RD, where RD represents rotor diameter of turbine [1,2]. Considering fact that rotor diameters of modern turbines commonly reach 100 m, spacing between turbines seems large but it is still necessary to reduce wake effects from upstream turbines on downstream ones which result in lower power higher load [3]. Investigations on active induction farm to reduce wake strength by regulating power of upstream turbines less than ir available power have been done by researchers. Earlier research articles were focused on phenomenon itself, physical explanations of power increase using one-dimensional momentum ory were presented [4,5]. The simple model predicted an increase of 4.1% of total power of two turbines in line when induction of first turbine was reduced from 1/3 to 1/5. In addition, to prove idea, tunnel tests micro scale turbine model arrays manually adjustable blade pitches a turbine spacing of 4.5 RD were used. From scaled farm measurements, about 4.5% of increase in total power was observed a blade pitch angle of 2. However, later, measurement uncertainty turned out to be greater than power increase 6%, results were also found to be greatly affected by Reynolds number effects occurring only in tunnel tests [5,6]. In addition, a few Appl. Sci. 2017, 7, 1068; doi: /app

2 Appl. Sci. 2017, 7, of 22 field measurements multi-mw turbines a 3.8 RD spacing were tried but not very successful [5,6] to make a quantitative conclusion. Later, field data were reanalyzed for two 2.5 MW turbines a spacing of 3.8 RD, turned out to have a power increase of about 5% a fixed pitch angle of 2 [7]. After that, active induction wake method based on manual blade pitch adjustment (change of axial induction) was applied to a simple turbine model an engineering wake model. The simulation concluded that adjustment of blade pitch angle can increase total power of a farm by about 10% [8 10]. Later, articles focused on various farm algorithms including genetic algorithms were presented [11 14]. Some were farm model based methods some were model free methods. Because it is very hard to prove farm effects on farm power output in real farms, all validations were made simulations. In addition, facts that a farm simulation must deal multiple turbines, simulations multiple turbines require large computational loads, limited simulations to be simulations of simple turbine models out any dynamics in conjunction a simple wake model. The turbine models were simply algebraic equations power coefficient simulations were made constant speeds no turbulence. The power gains from se simplified static analyses varied from 0.6% to 25% depending on method [11 14]. In addition, some researchers tried to change turbine operating point to reduce wake effect based on simple turbine models engineering wake models y concluded power increases of 1.86% to 6.24% [8]. However, results are considered to be exaggerated due to simple turbine model constant. Recently, an extremum seeking as a model-free method was used to prove algorithm. The simulation was done in turbulent s a few different turbulence intensities [15] but turbine model was still a simple algebraic equation that does not have any system dynamics. In study, ler was successful low turbulence intensity. However, ler failed high turbulence intensity which was about This was because, high turbulence intensity, ler could not distinguish power variations due to speed change from those due to farm. More recently, concept of power increase by regulating power of most upstream turbine was reinvestigated a relatively high fidelity farm simulation model [16]. In research, it was found that power partialization percentage, defined as power output to available power of most upstream turbine to achieve maximum power of a farm, is a function of turbine spacing but is not much affected by speed. Therefore, power regulation of upstream turbine for power maximization is achieved not by a constant blade pitch angle but by a constant power partialization of most upstream turbine. This means that blade pitch angle of most upstream turbine must be adjusted when speed varies. In study, a power increase of 4.1% was observed ten turbines in series a spacing of 4 RD when partialization of most upstream turbine was However, in that study, was not a turbulent but a constant, study did not include any farm algorithm, but did include results manual power adjustments of most upstream turbine for better understing of power increase mechanism. There have also been conflicting results in literature that active induction of most upstream turbine does not increase overall power output from farm [17,18]. They are mostly based on simulation results LES (Large Eddy Simulation) method. Nilsson et al. [17] applied LES to simulate Lillgrund farm showed simulation results ambient turbulence intensity of 5.6% that manual blade pitching of first turbine by multiples of 2 does not increase total power. However, in ir study, turbine dynamic was not considered. In addition, Annoni et al. [18] applied high fidelity simulation tool, SOWFA (Simulation for Wind Farm Applications) to investigate power increase of a small farm of five turbines by power curtailment. However, manual

3 Appl. Sci. 2017, 7, of 22 blade pitching of most upstream turbine did not increase total farm power, but rar decreased it approximately by 8.9%. In ir simulation results five turbines in series, powers of all four turbines except fifth one decreased power curtailment of most upstream turbine. They concluded that for total power increase, a suitable farm is necessary. Goit Meyers [19] applied an optimal strategy to LES simulation an actuator disk model to investigate total power increase of a farm 50 turbines. Unlike or LES simulations fixed blade pitching, y found power increase of 16% compared unled case. However, turbine dynamics power algorithms of turbines including blade pitch (most upstream turbine only) generator torque (all turbines) s were not considered in ir study. In this study, previous study [16] was furr exped to develop a model based farm method a simple but effective open-loop farm algorithm based on a function minimization method. The comm from farm ler to turbine was active power of turbine farm algorithm was validated relatively high fidelity farm model constructed previously [20] but modified for this study. The originality of this study compared previous research can be summarized by following factors. First, proposed farm in this study considered implementation of in actual farm lers that receive data from turbines give comms to m out affecting built-in turbine torque pitch algorithm for maximum power point tracking power regulation. By considering fact that modern multi-megawatt turbine lers use torque rotating speed of generator as targets to achieve power maximization or regulation, variable of most upstream turbine used for a farm was chosen not to be blade pitch angles or axial induction factors that used for previous articles [3 15] but to be generator power. The generator power can be expressed as torque multiplied by rotating speed of generator,, refore, power regulation of most upstream turbine could be done out affecting its built-in algorithm. However, turbine ler of turbine model in farm simulation tool had to be modified to additionally include a demed power tracking algorithm. Second, by considering fact that optimal power partialization of most upstream turbine is not much affected by speed [16], a simple open loop power comm output to turbines was implemented to farm ler. Modern multi-megawatt turbines already use open-loop for maximum power point tracking when speed is lower than rated speed [21 23]. In addition, a simple open-loop dispatch farm based on available powers of turbines in a farm is already implemented for a power dem tracking in farms. Therefore, a simple but effective open-loop farm for a total power increase in a farm was not new in this area was chosen for this study. The proposed farm ler consists of a speed estimator, optimal power comm calculator a farm model. It calculates available power at a chosen frequency, finds optimal power comms to individual turbines based on its farm model, sends comms to turbines for power increase load reduction. Thirdly, proposed farm algorithm was validated not a simple model out any dynamics under steady but a high fidelity farm simulation tool under turbulent turbine dynamics s. For this, turbine ler for power comm tracking as well as farm ler were constructed implemented to high fidelity simulation tool [16]. Therefore, more realistic simulation results can be obtained compared to previous studies [4 19] for validation of proposed farm algorithm.

4 Appl. Sci. 2017, 7, of Methodology 2.1. Wind Farm Simulation Tool Appl. Sci. For2017, simulations, 7, 1068 a farm simulation tool, which was constructed in previous study, 4 of was 22 modified to have communications farm ler. The farm ler has a algorithm based on on a a simplified farm farm model model embedded embedded in in ler ler performs performs a model a model predictive predictive.. The simulation The simulation tool is originally tool is originally based on based SimWindFarm on SimWindFarm [24] but[24] modified but modified to have to Ainslie s have Ainslie s eddy viscosity eddy viscosity wake model wake model new new turbine turbine farm lers farm lers [20,25]. It [20,25]. consists It consists of individual of individual turbine models, turbine wake models, model, wake model, propagation propagation model, model, generator, as generator, shown in Figure as shown 1. in Figure 1. Figure 1. Schematic of farm model used for simulation including turbine farm. Figure 1. Schematic of farm model used for simulation including turbine farm. The simulation tool was coded C language to achieve short simulation time. It can simulate load Thevariation simulation of tool a wasturbine coded for different C language toturbine achievestates shortsubjected simulation to time. different It can s, simulate but still load variation propagation of a is dependent turbine for on different Taylor s turbine frozen turbulence states subjected assumption. to different The individual s, but wake still fields generated propagation by is dependent turbines onare combined Taylor s frozen to be applied turbulence to downstream assumption. The individual turbines wake fields Sum generated of Squares by of turbines Velocity are Deficit combined equation. to be applied The equation to downstream is used for a turbines well-known commercial Sum of Squares farm of design Velocitytool Deficit [26] equation. is shown Thein equation Equation is(1). used for a well-known commercial farm design tool [26] is shown in Equation (1). n 1 2 Deficitn = ( Deficitkn) (1) k= 1 Deficit n = n 1 (Deficit kn ) 2 (1) The Sum of Squares of Velocity Deficit equation k=1 shown in Equation (1) is also known as quadratic sum model, where k represents total number of turbines constituting farm n The represents Sum of Squares number of Velocity of upstream Deficit equation turbines shown that make in Equation a wake effect (1) is also on m known [27]. as The quadratic speed sum deficit model, in Equation where k represents (1) is defined as total number of turbines constituting farm n represents number of upstream turbines that make a wake effect on m [27]. The speed deficit in Equation (1) is defined as ΔU Deficit = (2) U Deficit = U (2) U where ΔU means difference between freestream speed speed in wake region, U means freestream speed. where The U means turbine difference model is a between combination freestream of first or second speed order dynamic sub-models speed in [28] wake simulates region, U means turbine power freestream output tower speed. loads dynamically for turbulent s. The model receives The speed turbine model power is a combination comm from of first a or second farm order ler dynamic sub-models calculates electrical [28] power, simulates blade pitch turbine angle, power generator outputspeed, tower loads tower dynamically load. The forsimplified turbulent order s. sub Themodels include drive-train model, generator model, pitch actuator model, tower model, ler model. The aerodynamic conversion is calculated in an aerodynamic model which is a threedimensional look-up table a function of blade pitch angle tip speed ratio obtained from a commercial turbine dynamic simulation tool. The turbine ler includes a basic torque collective pitch a power dem tracking to achieve power output

5 Appl. Sci. 2017, 7, of 22 receives speed power comm from a farm ler calculates electrical power, blade pitch angle, generator speed, tower load. The simplified order sub models include drive-train model, generator model, pitch actuator model, tower model, ler model. The aerodynamic conversion is calculated in an aerodynamic model which is a three-dimensional look-up table a function of blade pitch angle tip speed ratio obtained from a commercial turbine dynamic simulation tool. The turbine ler includes a basic torque collective pitch a power dem tracking to achieve power output commed from a farm Appl. Sci. 2017, 7, of 22 ler. The validation of dynamic turbine model was made in previous study [20] Wind Farm Controller 2.2. Wind Farm Controller The farm ler constructed imbedded to farm simulation tool in this The farm ler constructed imbedded to farm simulation tool in this study study means a supervisory ler that collects turbine states sends comms to means a supervisory ler that collects turbine states sends comms to individual individual turbines to maximize farm power output. It receives blade pitch angle, turbines to maximize farm power output. It receives blade pitch angle, generator speed generator speed electrical power from individual turbines sends power comm to electrical power from individual turbines sends power comm to individual turbines. individual turbines. The farm ler consists of speed estimator, power comm calculator, The farm ler consists of a speed estimator, a power comm calculator, farm model. Figure is block diagram of relation between simulation tool for a farm model. Figure 2 is a block diagram of relation between simulation tool for verification of algorithm farm developed through this study. verification of algorithm farm developed through this study. Figure 2. Structure of farm ler. Figure 2. Structure of farm ler. The speed estimator estimates averaged speeds at rotor center of individual The turbines speed from estimator known estimates properties averaged measured signals speeds at from rotor center turbines. of individual Firstly, it calculates turbines aerodynamic from known torque properties from Equation measured (3) which is signals a two-mass from model turbines. equation of Firstly, motion it calculates for drive train aerodynamic available torque in literature from Equation [28]. (3) which is a two-mass model equation of motion for drive train available in literature [28]. ( J 2 ) d θr r + N Jg =Τa NΤg ( Br + N 2 Bg) θ (3) r dt In Equation (3), J is mass moment of inertia, N is gear ratio, θ r is rotational speed, T is torque, B is damping coefficient, d/dt is time derivative. In addition, subscripts

6 Appl. Sci. 2017, 7, of 22 ( ) d J r + N 2 θ ) r J g dt = T a NT g (B r + N 2 B g θ r (3) In Equation (3), J is mass moment of inertia, N is gear ratio, θ r is rotational speed, T is torque, B is damping coefficient, d/dt is time derivative. In addition, subscripts r, g, a represent rotor, generator aerodynamic, respectively. After finding aerodynamic torque, T a, from Equation (3), averaged speed V is obtained from Equation (4) [29]. T a = 1 ( ) CP (λ, β) 2 ρπr3 V 2 (4) λ In Equation (4), ρ is density of air, R is rotor radius, C P is power coefficient, λ is tip speed ratio, β is pitch angle, V is averaged speed. The power comm calculator from farm ler calculates available powers of turbines based on estimated speed obtained using Equations (3) (4) by using P av = T a θ r η m η g (5) where P is electrical power, η is efficiency. The subscripts av, m represent available mechanical, respectively [21]. The power comm calculator of farm ler n evaluates total available power from farm by adding all available powers finds individual power comms to turbines to maximize farm power as shown in Equation (6). It is a function minimization problem to find power comms to turbines to maximize total power output, farm model is used for function minimization process. In Equation (6), J is objective function to be used for function minimization process. J = TSOCMD WFC gen,i (6) The function minimization algorithm is an optimization algorithm that finds optimal variable values that can minimize objective function consisting of a single or multiple variables. Any function minimization algorithms can be used, but, in this study, Nelder Mead simplex algorithm was used for convenience in coding. The algorithm is a multidimensional unconstrained optimization algorithm for nonlinear optimization problems. In addition, it is one of most widely used function minimization algorithms as a direct search method. The method uses a simplex known as a polytope which has n + 1 vertices (or n + 1 test points) in n variables of objective function. To find values that can minimize objective function value, method repeats reflection, contraction expansion of variables [30,31] by comparing function values at n + 1 test points by avoiding test point that gives worst function value. More explanation to underst algorithm is available in [32]. In this study, TSO CMD in Equation (6) was chosen to be rated power of turbine multiplied by number of turbines because curtailment of total power from farm by TSO CMD is not considered. In addition, only one variable in objective function, power comm to most upstream turbine, was used to avoid convergence at a local minimum. In this case, refore, minimizing objective function in Equation (6) means that farm ler finds optimal power comm to most upstream turbine to maximize total power output (from three turbines) because TSO CMD will be always larger than available power from farm except case when speed is high enough to make all three turbines generate at ir rated powers. The farm model in farm ler was constructed to calculate total power output from farm for various operating conditions of most upstream turbine

7 Appl. Sci. 2017, 7, of 22 finally to find optimal power regulation of most upstream turbine by minimizing objective function, Equation (6), using a function minimization algorithm. Basically, farm model predicts speeds for all downstream turbines using Ainslie s eddy viscosity wake model estimates power output load variation according to power comm from power comm calculator to maximize power output by minimizing objective function. Changes based on TSO CMD are calculated for all turbines. The farm model in farm ler is a simplified version of farm model for dynamic simulation in Section 2.1. Figure 3 shows difference between farm model used for dynamic farm simulation simplified farm model applied to ler. As shown in figure, farm model applied to farm ler is similar to that used for dynamic farm simulation in a point that same wake wake merge models are applied to predict speed inside farm. However, farm model in farm ler differ from that Appl. Sci. 2017, 7, of 22 for dynamic farm simulation because it does not calculate propagation at each at time each step time step only calculates only calculates steady state steady s state at s rotors at ofrotors all downstream of all downstream turbines turbines by considering by considering wake wake wake superposition. wake superposition. Since time-dependent Since time-dependent propagation propagation algorithm is not algorithm used inis not used farm in model of farm model farm of ler, farm a simple ler, look-up a simple table based lookup table turbine based model out turbine model any out turbine any dynamics turbine was used. dynamics was used. Figure 3. Difference between farm simulation tool farm model on farm Figure 3. Difference between farm simulation tool farm model on farm ler: ler: (a) structure of dynamic farm simulation tool; (b) structure of simplified (a) structure of dynamic farm simulation tool; (b) structure of simplified farm model in farm model in farm ler. farm ler Wind Turbine Controller 2.3. Wind Turbine Controller The turbine ler in turbine model is based on comprehensive ler The turbine ler in turbine model is based on a comprehensive ler including MPPT (Maximum Power Point Tracking) torque for below rated speed including MPPT (Maximum Power Point Tracking) torque for below rated speed region, collective pitch PI for above rated speed region, driver-train damper region, collective pitch PI for above rated speed region, a driver-train damper to reduce in-plain torsional mode. The MPPT torque is for power maximization to reduce in-plain torsional mode. The MPPT torque is for power maximization collective pitch is for power regulation to rated power. For a given generator speed collective pitch is for power regulation to rated power. For a given generator speed (related (related to speed), blade pitch angle generator torque must be a constant value to speed), blade pitch angle generator torque must be a constant value to achieve to achieve power maximum or power regulation. When speed is lower than power maximum or power regulation. When speed is lower than rated rated speed, pitch angle is kept to be fine pitch known as pitch angle where speed, pitch angle is kept to be fine pitch known as pitch angle where aerodynamic aerodynamic conversion efficiency of rotor is maximized. At same time, generator torque conversion efficiency of rotor is maximized. At same time, generator torque that can achieve that can achieve maximum power is commed from turbine ler to maximum power is commed from turbine ler to generator. When generator. When speed is higher than rated speed, generator torque is kept to speed is higher than rated speed, generator torque is kept to be rated torque, be rated torque, blade pitch angle is PI led to target rated generator speed [21]. blade pitch angle is PI led to target rated generator speed [21]. As result, rated As result, rated power is maintained. power is maintained. For this study, turbine ler was needed to receive a comm from a higher level farm ler, a DPPT (demed power point tracking) algorithm was constructed implemented to basic turbine ler. When power comm from farm ler is larger than available power of turbine, turbine behaves like an ordinary turbine torque mode. Thus, if speed is lower than rated speed, pitch angle is fixed to be fine pitch generator torque

8 The three turbines were assumed to be in line direction turbine spacing was varied from 4 RD to 10 RD an interval of 3 RD. In addition, to find effect of Appl. Sci. 2017, 7, of 22 For this study, turbine ler was needed to receive a comm from a higher level farm ler, a DPPT (demed power point tracking) algorithm was constructed implemented to basic turbine ler. When power comm from farm ler is larger than available power of turbine, turbine behaves like an ordinary turbine torque mode. Thus, if speed is lower than rated speed, pitch angle is fixed to be fine pitch generator torque comm that produces maximum C P is sent. If speed is higher than rated speed, mode switch is turned on to make turbine operate in an ordinary pitch mode. In this mode, rated generator toque comm pitch comm to achieve rated rotational speed are sent from ler [21]. However, if power comm from farm ler to turbine ler is lower than available power of turbine, DPPT is turned on. The turbine ler n sends generator torque comm, which is equivalent to torque comm to achieve same power output in ordinary torque mode. This generator torque is lower than generator torque needed to achieve maximum efficiency in ordinary torque comm Appl. Sci. 2017, 7, of 22 mode at same speed, refore rotational speed of generator increases. Now generator increases. speed, Now which generator is equivalent speed, to which generator is equivalent speed to to achieve generator same speed power to achieve in ordinary same torque power in ordinary mode, is torque targeted in mode, collective is targeted pitch ler. in collective Therefore, pitch in ler. end, Therefore, generator in power, end, which generator is generator power, torque which multiplied is generator by generator torque multiplied speed, is by led generator to be speed, power is comm led from to be power farm comm ler. from The structure farm of ler. algorithm The structure imbedded of into algorithm turbine imbedded ler into of simulation turbine tool ler out of affecting simulation its conventional tool out torque affecting pitch its conventional logic is torque shown in Figure pitch 4 [21]. logic is shown in Figure 4 [21]. Figure 4. Structure of turbine ler [21] Conventional turbine ler torque Figure 4. Structure of turbine ler [21] Conventional turbine ler torque pitch pitch has has been been modified modified to to have have a DPPT DPPT algorithm algorithm to to receive receive power power dem dem from from a higher higher level level farm farm ler. ler Simulation 2.4. Simulation Simulations were performed to see effect of farm ler. For simulations, three Simulations were performed to see effect of farm ler. For simulations, three turbines in series aligned to were used. The reason why only three turbines turbines in series aligned to were used. The reason why only three turbines were chosen is because, in previous study, power regulation of most upstream were chosen is because, in previous study, power regulation of most upstream turbine turbine was found to be mostly effective on two downstream turbines [16]. was found to be mostly effective on two downstream turbines [16]. As turbine, 5-MW NREL research turbine was used. The specification of As a turbine, 5-MW NREL research turbine was used. The specification of turbine is shown in Table 1. turbine is shown in Table 1. The three turbines were assumed to be in line direction turbine Table 1. Specification of NREL 5-MW turbine model [21]. spacing was varied from 4 RD to 10 RD an interval of 3 RD. In addition, to find effect of proposed farm algorithm, Itemsonly most upstreamproperties turbine was used for power regulation to reduce calculation Rotor, time Hub Diameter avoid convergence in local 126 minimum. m, 3 m The simulation results Hub Height 90 m Cut-in, Rated, Cut-out speed 3 m/s, 11.4 m/s, 25 m/s Cut-in, Rated Rotor Speed 6.9 rpm, 12.1 rpm

9 Appl. Sci. 2017, 7, of 22 were compared results a baseline farm ler available in literature [33,34]. The baseline farm satisfies comm from transmission system operator when comm is lower than available power from farm. Each turbine receives its share in power generation as a power comm, which is proportional to its available power normalized by total available power in farm. If comm from TSO is higher than available power such as total rated power of farm which is case considered in this study, most upstream turbine will receive comm which is greater than its rated power. As result, most upstream turbine will extract as much energy from as possible, but limited to its rated power by pitch ler of turbine. The algorithm dispatches power comms to individual turbines based on ir own available powers, P av,i, as shown in Equation (7) [33,34]. WFC CMD,i = P av,i P av,i TSO CMD (7) Because turbines more available power (upstream turbines) get higher power comms from farm ler, baseline is also known as greedy. Therefore, it does not consider eir power increase or maximum load reduction in whole farm. Table 1. Specification of NREL 5-MW turbine model [21]. Items Rotor, Hub Diameter Hub Height Cut-in, Rated, Cut-out speed Cut-in, Rated Rotor Speed Properties 126 m, 3 m 90 m 3 m/s, 11.4 m/s, 25 m/s 6.9 rpm, 12.1 rpm 3. Simulation Result 3.1. Flow Field in Wind Farm Figure 5 shows snapshots of fields obtained from simulation for two different mean speeds turbulence out farm. Two different mean speeds are 8 m/s 10.5 m/s. The former is speed lower than rated speed of turbine, latter is speed close to rated speed of turbine. The is propagating from left to right, refore leftmost turbine is most upstream turbine. The three short vertical lines represent turbines. The spacing of turbines was 7 RD which is spacing of Danish Horns Rev 1 offshore farm. The turbulence intensity was Because of turbulence, speed fluctuates plots of Figure 5 shows speed distribution at a particular time at which speed at most upstream turbine is 8 m/s (for Figure 5a,c) or 10.5 m/s (for Figure 5e,g). The two plots in a row such as Figure 5a,b or Figure 5c,d show field in farm, steady fields normalized by free stream speed, respectively. The latter figure includes numbers above below steady fields. The former represents distance in rotor diameter from most upstream turbine latter represents largest speed deficit in wake region. The speed deficit increases when speed is furr reduced in wake region. In addition, it reduces as speed in wake region becomes recovered. In addition, a set of two rows such as row Figure 5a row Figure 5c or row Figure 5e row Figure 5g show results out farm proposed, respectively. In this case, out farm means greedy shown in Equation (7), it is baseline used in this study to validate performance of proposed farm.

10 Appl. Sci. 2017, 7, of 22 Appl. Sci. 2017, 7, of 22 Figure 5. Wind flow variation in farm baseline proposed farm lers Figure 5. Wind flow variation in farm baseline proposed farm lers under ambient turbulence intensity of 0.075: (a) turbulent field variation baseline under ambient turbulence intensity of 0.075: (a) turbulent field variation baseline farm ( U = 8 m/s); (b) steady speed deficit of figure (a); (c) turbulent field variation farm (U = 8 m/s); (b) steady speed deficit of figure (a); (c) turbulent field proposed farm ( U = 8 m/s) (d) steady speed deficit of figure (c); (e) variation proposed farm (U = 8 m/s) (d) steady speed deficit of figure (c); turbulent field variation farm ( U = 10.5 m/s); (f) steady (e) turbulent field variation baseline farm (U = 10.5 m/s); (f) steady speed speed deficit deficit of figure of figure (e); (e); (g) (g) turbulent field variation proposed proposed farm farm ( U (U = 10.5 = 10.5 m/s); m/s); (h) (h) steady speed deficitof offigure figure (g). (g). Red Reddashed dashed line line means means deficit deficit profile. Deviation from flat ends represents larger speed deficit. profile. Deviation from flat ends represents larger speed deficit. As shown in Figure 5b, where farm is off, wake generated between Itturbines can becauses seen in significant Figure 5a,c that speed reduction, wake area wake of expansion. turbine On is or clearly h, shown Figure 5d in downstream shows that, direction if is influence applied to continues turbine, to a relatively both effects long are distance. much reduced. Because Ainslie s eddy viscosity With model neglects speed of 10.5 pressure m/s, similar gradient effects in on near-wake flow region, by solution farm is not defined can inbe 2observed RD fromin Figure upstream 5e,g or Figure turbine 5f,h. [25]. Therefore, wakes begin at 2 RD behind turbine. In addition, it can be found from figures that, farm (Figure 5c), speed becomes higher in wake region than speed out farm. This is because when farm is turned on, most upstream turbine is forced to generate slightly lower power than power that it can generate by farm ler. Without farm (or baseline ), most upstream turbine generates electricity its best. This means that if farm is applied to most upstream turbine, wake impact on downstream turbines is reduced. This is also clearly shown in Figure 5b,d, which shows pure speed deficit only in space excluding turbulent components of out farm, respectively.

11 Appl. Sci. 2017, 7, of 22 As shown in Figure 5b, where farm is off, wake generated between turbines causes significant speed reduction, wake expansion. On or h, Figure 5d shows that, if is applied to turbine, both effects are much reduced. With speed of 10.5 m/s, similar effects on flow by farm can be observed in Figure 5e,g or Figure 5f,h. Appl. Sci. 2017, 7, of 22 Figure Figure 6. Wind 6. Wind flow flow variation in farm baseline proposed farm lers under ambient under turbulence ambient turbulence intensity intensity of 0.21: of (a) 0.21: turbulent (a) turbulent field field variation baseline farm farm (U = 8 m/s); ( U = 8 (b) m/s); steady (b) steady speed speed deficit deficit of figure of (a); (a); (c) (c) turbulent field variation proposed proposed farm farm (U = 8( m/s) U = 8 (d) m/s) steady (d) steady speed speed deficit deficit of figure of figure (c); (c); (e) turbulent (e) turbulent field variation field variation baseline baseline farm farm (U = 10.5 m/s); ( U = 10.5 (f) steady m/s); (f) steady speed deficit of figure (e); (g) turbulent field variation proposed farm (U = 10.5 m/s); speed deficit of figure (e); (g) turbulent field variation proposed farm ( U (h) = steady 10.5 m/s); speed (h) steady deficit of figure speed (g). deficit Red of dashed figure (g). line Red means dashed line means deficit profile. deficit Deviation fromprofile. flat Deviation ends represents from flat larger ends represents speedlarger deficit. speed deficit. Figure Figure 6 shows 6 shows exactly exactly same same plots plots as Figure as Figure 5 but 5 but higher higher ambient ambient turbulence turbulence intensity intensity of The higher ambient turbulence intensity can be noticed by severe changes of black of The higher ambient turbulence intensity can be noticed by severe changes of black white white colors in figure compared those in Figure 5. As shown in Figure 6a,c or Figure colors in figure compared those in Figure 5. As shown in Figure 6a,c or Figure 6b,d for 6b,d for mean speed of 8 m/s, speed deficit was reduced when proposed farm meanler speed was used. of 8 m/s, This result is speed same as deficit result was of reduced Figure when 5. The similar proposed results were farm ler obtained was for used. Figure This 6e,g result or Figure is 6f,h same that are as result cases when of Figure mean 5. The similar speed results was 10.5 were m/s. obtained for However, Figure 6e,g because or Figure of 6f,hmean that are speed, casesagain, when pitch mean was speed turned was 10.5 on for m/s. power However, because regulation, of mean as result, speed, again, speed deficits pitch was baseline turned on for proposed powerlers regulation, as were result, reduced compared speed deficits those at 8 m/s. It is known baseline that mixing proposed process lers of becomes were reduced more active ambient turbulence intensity, refore wake effect is eliminated more quickly compared Figure 5. As result, difference in speed deficit baseline ler proposed ler in Figure 6 was reduced compared Figure 5.

12 Appl. Sci. 2017, 7, of 22 compared those at 8 m/s. It is known that mixing process of becomes more active ambient turbulence intensity, refore wake effect is eliminated more quickly compared Figure 5. As result, difference in speed deficit baseline ler Appl. Sci. 2017, 7, of 22 proposed ler in Figure 6 was reduced compared Figure 5. Figure 7a,b shows speed gain for turbines when mean speed is is 8 m/s turbulence intensities are , respectively. As As shown in in figure, figure, input input speed speed of all of all turbines turbines located located downstream downstream is increased is increased by adjusting by adjusting power output power ofoutput most of upstream most upstream turbine turbine both cases. in both However, cases. However, as turbulence as turbulence intensity intensity increases, increases, speed increase speed percentage increase percentage decreases decreases by nearlyby two nearly times. two This times. phenomenon This phenomenon is relatedis torelated to speed recovery speed due recovery to due mixing to process mixing inprocess wake in region, wake which region, is known which is toknown be in proportion to be in proportion to ambient to turbulence ambient turbulence intensity intensity [25,35]. Since [25,35]. Since speed speed wake in region wake is rapidly region recovered is rapidly for recovered higher ambient for higher turbulence ambient intensity, turbulence intensity, speed reduction speed in reduction wake region in wake baseline region ler decreases baseline ler finally decreases speed finally gain speed proposed gain ler proposed decreases. ler Figure 7c,d decreases. shows Figure 7c,d speed shows gain for turbines speed when gain for mean turbines when speed is 10.5 mean m/s speed turbulence is 10.5 m/s intensities are turbulence intensities 0.21, respectively. are Because 0.21, respectively. mean Because speed is close mean to rated speed is close speed to of rated turbine, speed of which is 10.5 turbine, m/s, which pitch is 10.5 ler m/s, ispitch turned ler whenever is turned on whenever speed exceeds speed ratedexceeds speed. rated Therefore, speed. aerodynamic Therefore, performance aerodynamic of performance most upstream of most turbine upstream is reduced turbine to some is reduced extent to as some result extent power as output result adjustment power output of most adjustment upstreamof most turbine upstream baseline turbine ler baseline becomes ler less effective becomes on increasing less effective on increasing speeds of downstream speeds of downstream turbines [16]. turbines [16]. Figure Figure Wind Wind speed speed variation variation for for downstream downstream turbines turbines baseline baseline proposed proposed farm lers: (a) ambient speed conditions: U = = 88 m/s, TI = 0.075; (b) ambient speed conditions: U = = 88 m/s, TI = 0.21; (c) ambient speed conditions: U = = m/s, TI = 0.075; (d) ambient speed conditions: U = 10.5 m/s, TI = (d) ambient speed conditions: = 10.5 m/s, TI = Total Power Figure 88 shows shows dynamic dynamic simulation simulation results results both both baseline baseline ler ler proposed ler. proposed ler. The turbulence The turbulence intensity intensity was fixed was tofixed be to be frequency frequency was was 0.1 Hz. 0.1 The Hz. spacing The spacing of of turbines turbines was 7was RD, 7 which RD, which is turbine is turbine spacing spacing of well-known of well-known Danish Danish Horns Rev offshore farm. The mean speed to most upstream turbine was fixed to be 8 m/s. Power comms from farm ler to individual turbines both proposed baseline were calculated from Equations (4) (5), respectively. Figure 8a,f shows turbulent s to three turbines in series. For two different

13 Appl. Sci. 2017, 7, of 22 Horns Rev offshore farm. The mean speed to most upstream turbine was fixed to be 8 m/s. Power comms from farm ler to individual turbines both proposed baseline were calculated from Equations (4) (5), respectively. Appl. Figure Sci. 2017, 8a,f 7, 1068 shows turbulent s to three turbines in series. For two different 13 farm of 22 lers, s to most upstream turbines are same, but those to subsequent subsequent turbines are slightly turbines different. are slightly This different. is because This is because most upstream most upstream turbine is operated turbine is differently operated differently different different farm lers farm lers refore refore s passing s through passing turbine through are affected turbine differently. are affected Figure differently. 8b,g shows Figure 8b,g power shows comms power to individual comms to individual turbines by turbines farmby lers. farm For lers. baseline ler, For baseline power ler, comm power to most comm upstream to most turbine upstream is ratedturbine power, is 5 MW, rated, power, refore, 5 MW,, turbine refore, will extract turbine power from will extract power as much from as possible. as The much power as possible. comms The topower downstream comms two to turbines downstream are much two lower turbines are dependent much lower on ir available dependent powers. on ir Foravailable proposed powers. ler, For proposed power comm ler, to power most upstream comm to turbine most upstream is less than 5 MWturbine to pass is slightly less than more 5 MW to topass downstream slightly more turbines. to downstream In addition, power turbines. comms In addition, to downstream power comms turbinesto aredownstream both 5 MW, refore turbines are downstream both 5 MW, turbines refore will extract downstream power from turbines as much will extract as possible. power from as much as possible. Figure 8. Dynamic simulation results in time domain baseline proposed farm Figure 8. Dynamic simulation results in time domain baseline proposed farm lers: lers: (a,f) speed used in comparison of baseline ler proposed ler for (a,f) speed used in comparison of baseline ler proposed ler for three three turbines; (b,g) power comm according to use of baseline ler proposed turbines; (b,g) power comm according to use of baseline ler proposed ler to each ler to each turbine (c,h) blade pitch angle change of each turbine according to use turbine (c,h) blade pitch angle change of each turbine according to use of baseline ler of baseline ler proposed ler; (d,i) generated power variation of turbines proposed ler; (d,i) generated power variation of turbines according to use of baseline according to use of baseline ler proposed ler ; (e,j) total power variation of ler proposed ler ; (e,j) total power variation of farm according to use of baseline farm ler according to proposed use of baseline ler. ler proposed ler. The blade pitch angles are presented in Figure 8c,h. For baseline ler, blade pitch The blade pitch angles are presented in Figure 8c,h. For baseline ler, blade pitch angles are zero (fine pitch angle), no blade pitch is used. This is because turbulent angles are zero (fine pitch angle), no blade pitch is used. This is because turbulent does not reach rated speed of turbine. All three turbines extract as does not reach rated speed of turbine. All three turbines extract as much much power from as possible. For proposed ler, pitch angles of two downstream turbines are zero, but pitch angle of most upstream turbine varies in 0 4. This is because most upstream turbine is slightly pitched to pass more to downstream turbines downstream turbines do ir best to extract power from. This result is similar to result obtained from a tunnel miniature turbines fixed blade pitching by or researchers that pitch angle of 2 was best to

14 Appl. Sci. 2017, 7, of 22 power from as possible. For proposed ler, pitch angles of two downstream turbines are zero, but pitch angle of most upstream turbine varies in 0 4. This is because most upstream turbine is slightly pitched to pass more to downstream turbines downstream turbines do ir best to extract power from. This result is similar to result obtained from a tunnel miniature turbines fixed blade pitching by or researchers that pitch angle of 2 was best to maximize farm power [4,7]. However, optimum pitch angle found in this study was not fixed to be 2 but varied speeds or available power. In frequency domain analysis, it is known that most energy components in turbulent s are included in 1 rad/s, refore bwidth of pitch ler is commonly chosen to be around or higher than 1 rad/s to cover speed variations [36]. In addition, bwidth of pitch actuators is commonly higher than 100 rad/s, refore, as shown in Figure 8b,h, pitch actuator responds rapidly according to speed variations [21]. Figure 8d,i shows electrical power generated from turbines for both baseline proposed farm lers. For most upstream turbines, one proposed generates slightly less power than or baseline. However, for two downstream turbines, those proposed ler generate more than those baseline ler. In end, for total power, proposed farm ler yields more power than baseline ler, as shown in Figure 8e,j Tower Load Figure 9 shows simulation results for tower load. More specifically, load is fore aft bending moment at tower bottom. The mean speed, turbulent intensity, frequency, turbine spacing were 8 m/s, 0.075, 0.1 Hz, 7 RD, respectively. Figure 9a,c shows dynamic tower loads in time domain for three turbines baseline ler proposed ler, respectively. As can be seen from figures, most upstream turbine, regardless of farm lers, experiences most tower load. In addition, it is shown that proposed farm ler, dynamic tower load of most upstream turbine has been substantially reduced. Figure 9b,d shows loads expressed in DEL (damage equivalent load) to express fatigue load by periodic loads in a constant value for comparison. The DEL is mamatically defined as n k Sk m D eq = k N eq In Equation (8), S k is stress of tower load having kth magnitude out of total tower load during a specified evaluation (simulation) time (commonly 600 s), n k is number of cycle of S k N eq is equivalent number of cycle of D eq which is commonly calculated from evaluation (simulation) time multiplied by a frequency of 1 Hz. In addition, in Equation (8), m is a constant related to slope of SN (stress to cycles to failure) curve of a material. For a steel tower, value of 3.5 is used as m [36]. With baseline farm ler, most upstream turbine has highest load, it is about 417 kn m. However, proposed farm ler, most upstream turbine is commed to generate power slightly less than that turbine can actually generate. Therefore, most upstream turbine passes more to downstream turbines. As result, load of most upstream turbine decreased to 347 kn m. However, loads of two downstream turbines baseline ler, which were 260 kn m 226 kn m, increased to 285 kn m 236 kn m proposed ler. Although loads of two downstream turbines increase, y are small compared to load of most upstream turbine. Therefore, largest load proposed ler becomes lower than largest load baseline ler. The load reduction proposed 1 m (8)

15 Appl. Sci. 2017, 7, of 22 ler was not targeted in proposed farm but it can be automatically achieved by reducing power generation of most upstream turbine. As result, proposed farm ler is found to levelize load distribution of turbines to some extent in Appl. Sci. farm. 2017, 7, of 22 Figure Simulation Result for for Load (a,c) (a,c) turbine tower load load variation in in time time domain when applying baseline ler proposed ler; (b,d) turbine tower DEL variation when applying baseline ler proposed ler Discussion Discussion To To fully fully underst underst proposed proposed farm farm,, parametric parametric studies studies for for turbine turbine spacing, spacing, turbulent turbulent intensity intensity frequency frequency were were made. made Total Power Figures show power increase of of proposed farm in in percentage relative to to power of of baseline for for two different mean speeds, 88 m/s, 10.5 m/s. Figure 10a,b presents 8 m/s, Figure 11a,b presents 10.5 m/s. Two speeds were chosen for simulation because y correspond to two different turbine regions. The former is is region for for maximum maximum power power coefficient coefficient latter is latter is region forregion smoothfor transition smooth from transition maximum from maximum power coefficient power coefficient rated power rated. power Based. Based figure, on it can figure, be found it can be that found power that increase power of increase proposed of proposed ler ler respect torespect power to power baseline ler baseline ler decreases decreases increasing increasing turbine spacing turbine spacing increases increases increasing increasing frequency. frequency. In addition, In addition, power increase power decreased increase decreased when ambient when turbulent ambient turbulent intensity increased. intensity increased. This is partly This because is partly morebecause fluctuations more influctuations speed make in power speed comms make power to comms turbines to deviate more turbines fromdeviate optimal more values. from Inoptimal addition, values. this is In because, addition, this is because, higher turbulence higher intensity, turbulence wake intensity, recovers more wake rapidly, recovers, more refore, rapidly,, wake refore, loss wake baseline loss ler baseline is reduced ler finally is reduced effect of active finally induction effect of active is reduced. induction The power is reduced. increase was The reduced power increase when was reduced speed is when in transition speed is in region. transition When turbineregion. spacingwhen reached 10 turbine RD, nospacing power increase reached was 10 RD, observed. no power It is because increase when was observed. turbineit spacing is because is 10 RD, when wake turbine loss due spacing to is upstream 10 RD, wake turbine loss due is small. to Inupstream addition, when turbine turbulence is small. intensity In addition, was large when such as turbulence 0.21, intensity power was large proposed such as 0.21, became power even lower proposed than powerbecame even baseline lower ler than power when baseline frequency ler was 0.01when or This is because frequency was farm 0.01 or ler This runs is too because slow to effectively farm track ler high runs too speed slow to variation. effectively track high speed variation. However, when frequency was 0.1 Hz, although ambient turbulence intensity was 0.21, power proposed became higher again than power baseline. When turbine spacing was 7 RD increase was 2.29%. In previous study an extremum seeking [15], a similar decreasing trend in power gain relative to baseline was observed increasing ambient turbulent intensity, but, ambient turbulence

16 Appl. Sci. 2017, 7, of 22 intensity of 0.21, ler failed to distinguish change in power by from change in Appl. Sci. 2017, 7, of 22 power Appl. Sci. by 2017, turbulence, 7, 1068 power gain became negative. 16 of 22 Figure 10. Simulation Results for Power at 8 m/s: (a) power increase farm frequency Figure 10. Simulation Results for Power at at 8 m/s: (a) power increase farm frequency turbine spacing (fixed condition: TI = 0.075, U = 8 m/s), (b) power increase change turbine spacing (fixed (fixed condition: TI = TI 0.075, = 0.075, U = U 8 m/s), = 8 m/s), (b) power (b) power increase increase change change farm frequency ambient turbulence intensity (fixed condition: spacing = 7 RD, U = farm farm frequency frequency ambient ambient turbulence turbulence intensity intensity (fixed condition: (fixed condition: spacing = spacing 7 RD, U = = 7 RD, 8 m/s). U = 8 m/s). 8 m/s). Figure Figure Simulation Simulation Results Results for for Power Power at 10.5 at 10.5 m/s: m/s: (a) (a) power power increase increase farm farm frequency Figure 11. Simulation Results for Power at 10.5 m/s: (a) power increase farm frequency turbine turbine spacing spacing (TI (TI = 0.075, = 0.075, U = U 10.5 = 10.5 m/s), m/s), (b) (b) power power increase increase farm farm frequency turbine spacing (TI = 0.075, U = 10.5 m/s), (b) power increase farm frequency frequency ambient ambient turbulence turbulence intensity intensity (spacing (spacing = 7 = RD, 7 RD, U = U 10.5 = 10.5 m/s). m/s). frequency ambient turbulence intensity (spacing = 7 RD, U = 10.5 m/s) Tower However, Load when frequency was 0.1 Hz, although ambient turbulence intensity However, when frequency was 0.1 Hz, although ambient turbulence intensity was was Figure 0.21, 0.21, 12 power shows power variation proposed proposed of tower loadbecame higher respect higher again to again than than turbulence power power intensity baseline.. frequency. When When Itturbine isturbine result spacing spacing whenwas was 7 RD turbine 7 RD increase spacing increase is was 7was RD. 2.29%. 2.29%. As shown In In inprevious figure, study study for all an an cases, extremum extremum tower seeking seeking load of [15], most [15], a upstream a similar similar decreasing decreasing turbinetrend trend decreased in power gain relative to baseline in power dramatically. gain relative to baseline was observed increasing ambient turbulent intensity, but, ambient turbulence Although was observed loads of increasing downstream ambient turbulent turbines intensity, increased, but, y were kept ambient lower turbulence than load intensity of 0.21, ler failed to distinguish change in power by from change intensity of of most 0.21, upstream ler failed turbine. to distinguish However, as change in frequency power by increased from from 0.01 change Hz to in power by turbulence, power gain became negative. in 0.1 power Hz, by turbulence, tower load increased. power This gain is because became negative. turbine uses more pitch action higher frequency. In addition, it can be found from figure that decrease in tower load 4.2. Tower Load of 4.2. Tower mostload upstream turbine decreases increasing ambient turbulent intensity. When ambientfigure turbulence 12 shows intensity was variation 0.21, of tower decrease load from respect value ofto baseline turbulence ler intensity was 9.2%. Figure 12 shows variation of tower load respect to turbulence intensity The reasonfrequency. why tower It is loadresult increases when turbine ambient spacing turbulent is 7 RD. intensity As shown is because in figure, more pitching for all frequency. It is result when turbine spacing is 7 RD. As shown in figure, for all action cases, occurs tower in load blade of of most upstream turbine. turbine decreased dramatically. cases, tower load of most upstream turbine decreased dramatically.

17 Appl. Sci. 2017, 7, of 22 Appl. Sci. 2017, 7, of 22 Figure 12. Simulation SimulationResults Results for for Tower Tower Load Load Variation: Variation: (a c) (a c) DEL DEL variation variation of of first firstturbine turbine according according to turbulence to turbulence intensity intensity farm farm frequency: frequency: 0.01, 0.05, 0.01, , Hz); 0.1 (d f) Hz); DEL (d f) variation DEL variation of ofsecond second turbine turbine according according to toturbulence turbulenceintensity farm farm frequency: frequency: 0.01, 0.01, 0.05, 0.05, Hz) Hz) (g i) (g i) DEL DEL variation variation of of third third turbine turbine according according to to turbulence turbulence intensity intensity farm farm frequency: frequency: 0.01, 0.01, 0.05, 0.05, Hz). Hz) Comparison Although of Or loads Models of downstream turbines increased, y were kept lower than load Table of 2 most shows upstream experimental turbine. However, simulation as results of frequency farm increased available from 0.01 in Hz literature to 0.1 Hz, for comparison. tower load A increased. few experimental This is because results are available turbine in literature. uses more Based pitch on action table, higher results from frequency. experiment In addition, showed it 4.5 5% can be found increase from in power figure that manual decrease blade in pitching tower load of 2 of. However, most upstream more experimental turbine validation decreases seems necessary increasing for ambient a concrete turbulent conclusion. intensity. The simulation When results ambient using turbulence Jensen intensity linear wake was 0.21, model showed decrease relatively from high value power of increase. baseline They ler were 10.57% was increase 9.2%. The reason an optimal why tower load 6.24% increases increase ambient a manual turbulent adjustment intensity of is tip because speed more ratio (TSR). pitching The action simulation occurs results in blade more of advanced turbine. wake models based on Parabolic Reynolds averaged Navier Stokes equation (RANS) such as Ainslie s eddy viscosity model or UPMWAKE showed 4.3. Comparison of Or Models power increase from 2.85% to 4.5% depending on turbine spacing type. The power increases Table were 2 shows slightly lower experimental than those fromsimulation Jensenresults wake of model because farm Jensen available modelin literature wake decay for comparison. constant of 0.04 A few (recommend experimental for offshore results are application) available isin known literature. to be equivalent Based on to ambient table, turbulence results from intensity experiment of approximately showed 8 10% % Therefore, increase in wake power recovery manual Jensen blade model pitching is expected of 2. However, to be faster more than experimental wake recovery validation in seems location necessary wherefor a turbulence concrete conclusion. intensity is The lower simulation than. The results using from Jensen LES simulation linear wake model an actuator showed disk relatively or an actuator high power lineincrease. method showed They were some 10.57% discrepancy. increase The manual optimal blade pitching 6.24% increase LES simulation a manual showed adjustment power of decreases tip speed of 2.2% ratio (TSR). 8.9% The simulation blade results pitching more angle advanced of 2. The wake largest models decrease based inon Parabolic power Reynolds averaged Navier Stokes equation (RANS) such as Ainslie s eddy viscosity model or UPMWAKE showed power increase from 2.85% to 4.5% depending on turbine spacing

18 Appl. Sci. 2017, 7, of 22 was obtained five turbines in series, blade pitching of most upstream turbine, power outputs from four upstream turbine were all decreased power of fifth turbine was slightly increased compared reference baseline. The reason for this unusual result is explained to be due to wake meering effect by authors of literature. Unlike two results LES simulation, optimal algorithm applied to LES simulation an actuator disk model showed power increase of 16%. However, turbine algorithms turbine dynamics were not used in study this might reduce power increase. Although results in Table 2 were obtained different conditions cannot be compared directly, results in this study proposed model predictive open loop farm are in range of results obtained from or studies, power gains are relatively low. The reason for this is considered to be that more realistic conditions including turbine dynamics, turbine, farm frequency have been added in simulation of this study. However, experimental study must be done as a future work to fully validate result Strengths Weaknesses of Current Study The strengths of current study can be summarized as following two points. First, current study considered actual application of farm algorithm to real farm, proposed a simple but effective open loop farm which can be fully applied to a farm ler (hardware) used out affecting current turbine algorithms. The farm ler receives measured generator speed, blade pitch angle, power output from turbine ler sends optimal power comm to turbine ler. In addition, proposed farm algorithm is validated in a relatively high-fidelity farm simulation tool [20], which has turbine dynamics including basic torque pitch ler slightly modified to include demed power point tracking (DPPT) [37]. Therefore, current farm simulation is almost same as real farm in terms of. In addition, farm frequency applied was at least 10 times slower than variation in simulation to get more realistic simulation results. Parametric studies including turbulence intensities, turbine spacing farm frequencies were made to see effects of active induction on total power increase in some more detail. This study also has some weaknesses y are as follows. The wake model used in this study is Ainslie s eddy viscosity model which uses thin boundary layer approximation on RANS (Reynolds Averaged Navier Stokes) equation empirical boundary condition obtained from tunnel test. Although model or its modified version is commonly used in commercial software for calculating power outputs of a single or multiple turbines including wake effect, accuracy of model needs to be experimentally validated various turbine operating conditions. In addition, in this study, wake meering is not considered. If meering is considered, power decrease in wake region might be changed, refore, performance of proposed farm algorithm will be affected. Therefore, in future, experimental validation of simulation results needs to be performed.

19 Appl. Sci. 2017, 7, of 22 Table 2. The Trend of Active Wake Control Validation (RD: rotor diameter, TSR: tip speed ratio, Pav: available power, WT: turbine, DPPT: demed power point tracking). Researcher Wind Farm Model Wind Spacing Induction Control Type Power Increase Boorsma K. [7] Corten G.P. [5] De-Prada-Gil M. [8] Behnood A. [9] Lee J. [10] Kim H. [16] Horvat T. [11] Clevenhult T.A. [12] Nilsson K. [17] Annoni J. [18] Goit J.P. [19] Current Study - Real farm Turbulent 3.5 RD - Manual Pitch - WT. model: Full size (2.5 MW) - Wind Tunnel Turbulent 4.5 RD - Manual Pitch - WT model: Scaled model - Jensen wake model Constant 7.0 RD - Manual TSR +6.24% - WT. model: BEM (no WT. dynamics) - Jensen wake model Turbulent 3.3 RD - Optimal algorithm % - WT. model: BEM (no WT. dynamics) - Ainslie wake model Constant 7.0 RD - Optimal Algorithm +4.50% - WT. model: BEM (no WT. dynamics) - Ainslie wake model Constant 4.0 RD - Manual Power Comm P - WT. model: BEM (include WT. dynamics &) av - UPMWAKE Constant 3.2 RD - Optimal Algorithm +2.85% - WT. model: BEM (no WT. dynamics) - Madjidian Rantzer Turbulent 5.0 RD - Optimal Algorithm +3.26% - WT. model: BEM ( WT. dynamics & ) - Large Eddy Simulation Turbulent 3.3 RD4.3 RD - Manual Pitch - WT. model: Actuator disk - Large Eddy Simulation Turbulent 5.0 RD - Manual Pitch Approx. - WT. model: Actuator Line coupled FAST - Large Eddy Simulation Turbulent 4.0 RD - Optimal Algorithm % - WT. model: Actuator disk - Ainslie wake model RD Turbulent 4.0 RD~10.0 RD - Optimal Algorithm - WT. model: BEM ( WT. dynamics & including DPPT) RD RD

20 Appl. Sci. 2017, 7, of Conclusions A simple but effective model based open-loop farm algorithm was proposed validated a relatively high-fidelity simulation tool in this study. The farm model for was a simplified version of farm model for dynamic simulation, consisted of a turbine model, wake model, propagation superposition model, a farm model. Unlike farm model for simulation, it had a look-up table based turbine model that does not have turbine dynamics. The farm model was used in farm ler to find optimal power comms to individual turbines. With a function minimization algorithm, farm algorithm was validated dynamic simulations turbulent s. For a virtual farm three turbines in series, simulations were performed for both baseline proposed farm lers at mean speed of 8 m/s. As result, proposed farm ler was found to increase total power output by about 2.4% to decrease load of most upstream turbine in DEL by about 16.7% when turbulence intensity was frequency was 0.1 Hz. In addition, parametric studies were performed to underst power increase of proposed farm ler compared baseline ler for different turbulent speeds different turbulence intensities different frequencies. The power increase was found to decrease as turbine spacing increases it decreased to about 1.5% when turbine spacing was 10 RD at mean speed of 8 m/s. In addition, power increase was found to decrease as turbulence intensity increases it decreased to about 0.83% when turbulence intensity was increased to When mean speed was close to rated speed, power increase was also found to decrease. Although no direction change was considered in current study, effect of model based farm algorithm was found to be effective to increase power reduce load in a farm. However, in current study, Ainslie s eddy viscosity wake model was used to calculate speed in wake region, Taylor s frozen turbulence assumption was used for speed propagation. Therefore, results need to be experimentally validated in a tunnel or in field for better understing. Acknowledgments: This work was supported by Human Resources Program in Energy Technology New & Renewable Energy Core Technology program of Korea Institute of Energy Technology Evaluation Planning (KETEP) granted financial resource form Ministry of Trade, Industry & Energy, Korea (Nos ). Author Contributions: H.K. developed farm simulation tool farm ler; K.K. constructed turbine model developed turbine ler; I.P. H.K. analyzed simulation results wrote paper. Conflicts of Interest: The authors declare no conflict of interest. References 1. Rodrigues, S.; Restrepo, C.; Kontos, E.; Pinto, R.T.; Bauer, P. Trends of offshore projects. Renew. Sustain. Energy Rev. 2015, 49, [CrossRef] 2. 4C Offshore. Offshore Wind Farms. Available online: (accessed on 1 June 2017). 3. Barlmie, R.J.; Pryor, S.; Frsen, S.T.; Hansen, K.S.; Schepers, J.; Rados, K.; Schelez, W.; Neubert, A.; Jensen, L.E.; Neckelmann, S. Quantifying impact of turbine wakes on power output at offshore farms. J. Atmos. Ocean. Technol. 2010, 27, [CrossRef] 4. Gustave, P.C.; Pieter, S. Heat flux: Increase of farm production by reduction of axial induction. In Proceedings of European Wind Energy Conference, Madrid, Spain, June Corten, G.P.; Schaak, P. More power less loads in farms. In Proceedings of European Wind Energy Conference, London, UK, November 2004.

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