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Nonlinear Model Predictive Control of a Point Absorber Wave Energy Converte

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118 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 4, NO. 1, JANUARY 2013 Nonlinear Model Predictive Control of a Point Absorber Wave Energy Converter Markus Richter, Mario E. Magaña, Senior Member, IEEE, Oliver Sawodny, and Ted K. A. Brekken, Member, IEEE Abstract—This paper presents the application of nonlinear model predictive control (NMPC) to a point absorber wave energy converter (WEC). Model predictive control (MPC) is generally a promising approach for WECs, since system constraints and actuator limits can be taken into account. Moreover, it provides a framework for deﬁning optimal energy capture and it can beneﬁt from predictions. Due to possible nonlinear effects, such as the mooring forces, an NMPC is proposed in this paper, whose performance is compared to that of a linear MPC. Both controllers are supposed to control a nonlinear point absorber model. Computer simulations show that the proposed NMPC is able to optimize the energy capture while satisfying system limits. Index Terms—Nonlinear model predictive control, point absorber, wave energy converter. I. INTRODUCTION O CEAN wave energy is a promising renewable energy source that can be converted into useful electrical energy using WECs. In general, it is difﬁcult to estimate the amount of exploitable wave energy in the world’s ocean. According to [1], the ocean holds approximately 8000–80 000 TWh/year or 1–10 TW, whereas Falnes in [2] quantiﬁes the world’s exploitable wave power resource to be of the order of 1 TW. Comparing this to the world’s annual energy consumption of approximately 148 000 TWh in 2008, [3] shows that wave energy could play an important role in the world’s energy portfolio. Furthermore, most renewable energy resources are variable and nondispatchable and thus have a signiﬁcant impact on the utility reserve requirements. Halamay et al. showed in [4] that this impact can be reduced by a diversiﬁed energy mix, where WECs could play an important role. In order for WECs to become a commercially viable alternative, operating the WEC in an optimal fashion is a key task. Much work on optimizing the energy generation of WECs has been done which leads to control laws such as latching control and phase and amplitude control [5]–[7]. Manuscript received November 08, 2011; revised May 11, 2012; accepted May 21, 2012. Date of publication July 31, 2012; date of current version December 12, 2012. M. Richter and O. Sawodny are with the Institute for System Dynamics, University of Stuttgart, Stuttgart 70049, Germany (e-mail: markus-c.richter@arcor.de; sawodny@isys.uni-stuttgart.de). M. E. Magaña and T. K. A. Brekken are with Oregon State University, Corvallis, OR 97331 USA (e-mail: magana@eecs.oregonstate.edu; brekken@eecs. oregonstate.edu). Color versions of one or more of the ﬁgures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identiﬁer 10.1109/TSTE.2012.2202929 Furthermore, Schoen et al. [8] have proposed a hybrid control strategy in order to increase the energy conversion while being robust to modeling errors. The short-term tuning of the converter is done by a fuzzy logic controller, while the robust controller attempts to minimize the modeling errors. Recently, researchers increasingly deal with MPC algorithms in order to control point absorbers. They have shown that MPC is a very promising control method, since it is able to exploit the entire power potential of a WEC on the one hand, while respecting the constraints on motions and forces on the other hand. All the above-mentioned control approaches require prediction data of the wave’s motion or at least they can beneﬁt from predictions. The problem of short-term wave forecasting has already been studied by many researchers. Fusco and Ringwood [9], for example, focused on wave forecasting and prediction requirements for unconstrained phase and amplitude control. In that work, the required forecasting horizon and the achievable performance of wave predictions for different properties of the ﬂoating system has been quantiﬁed. The same authors presented several wave prediction algorithms in [10]. They implemented cyclical models, autoregressive models, and neural networks in order to predict the wave’s motion and validated these models against real observations from data buoys. It was shown that an accurate prediction for up to two typical wave periods into the future can be calculated. Different prediction models have been presented in [8]. There, a predictive Kautz model and a combination of a Kautz and an autoregressive model have been proposed, where both ﬁlters have reduced orders compared to conventional prediction ﬁlters. Hence, the proposed ﬁlters can lower the computational effort in real-time applications. This paper deals with model predictive control, where a prediction of the wave’s motion is required. However, it does not focus on wave prediction and an ideal prediction is assumed. However, the above-mentioned work on wave prediction could be used to estimate the wave horizon for our model predictive controller as well. Current research into MPC for wave energy applications exclusively focuses on linear MPC. Brekken in [11] and Hals et al. in [12] successfully applied linear MPC to one-body WEC models. Moreover, the application to a linear two-body model with mooring was demonstrated in [13]. In order to deal with possible nonlinear effects, such as the mooring forces, a nonlinear model predictive controller is proposed in this paper. Its performance is compared to that of a linear MPC, also controlling the nonlinear system. The proposed controllers are validated and compared through simulation for irregular sea states. 1949-3029/$31.00 © 2012 IEEE RICHTER et al.: NONLINEAR MODEL PREDICTIVE CONTROL OF A POINT ABSORBER WAVE ENERGY CONVERTER 119 culate the radiation forces. Using impulse response functions yields convolution terms in the expressions for the radiation forces. Furthermore, a highly nonlinear mooring force law [17] is used. The motion of the buoy and the spar is denoted by and , respectively. The equation of motions (EOMs) can be derived using Newton’s second law. The formulation is based on linear wave theory (LWT) and the frequency-dependent parameters of the L10 are assumed to be constant. The EOMs for the two bodies are (1) (2) where the radiation forces , the hydrodynamic force , and the mooring force are described by (3) (4) (5) (6) (7) Fig. 1. L10 Wave Energy Coverter [14] (top) and schematic diagram (bottom). (8) The paper is organized as follows. In Section II, a nonlinear two-body model in the time-domain is presented, where the discretization of the nonlinear model is also discussed. The implementation of the NMPC is described in Section III. By means of computer simulation, the performance of the proposed NMPC is validated and compared to that of the linear MPC from the work in [13]. II. POINT ABSORBER MODEL This paper exclusively focuses on one subclass of WECs, namely, point absorbers. The point absorber L10, developed at Oregon State University is considered in this work and is shown on the left side of Fig. 1. It consists of a ﬂoat, also called buoy, ﬂoating on the ocean surface and a second body consisting of a spar and a ballast tank (in what follows, just spar), where the spar’s motion is damped through mooring. The relative motion of the two bodies can be converted into usable energy through a power takeoff (PTO) system. A schematic diagram of the L10 is shown at the top of Fig. 1, where and denote the positions of the spar and the ﬂoat, respectively. The readers are referred to [15] for detailed information about the L10. A. Equations of Motion The proposed nonlinear two-body model follows the work in [16]. Thus, impulse response functions are used in order to cal- where the impulse response functions of the different radiation forces are denoted by . The hydrodynamic parameters are the added masses, are the viscous damping factors, and are the hydrostatic stiffnesses. Also, there are coupled ra- diation forces resulting from the interaction of the spar and the buoy. denotes the PTO force which is the control input for the system, whereas the excitation forces and are the system disturbances. The mooring system is based on the experimental mooring conﬁguration in [17]. A top view schematic diagram is shown in Fig. 2. There, the buoy is moored to a static reference around the buoy. There are two layers of cables, where each layer consists of four cables as can be seen in Fig. 2. Here, no mooring to the sea ﬂoor is assumed. is the stiffness of one cable, and is the horizontal length from the reference to the buoy. Table I lists the parameters used for modeling, where a cable stiffness of N m and a cable length to the reference of m is assumed for the mooring force calculation. In what follows, we neglect the convolution terms because the inertia and the damping term are the dominating terms for the ra- diation forces. The convolution terms are fairly small compared to other external forces. Simulation results conﬁrm that assump- tion. In case there was a larger inﬂuence of these terms, the 120 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 4, NO. 1, JANUARY 2013 can be formulated. Thereby, it is assumed that the entire state is measurable. The model can be written as (13) where Fig. 2. Mooring conﬁguration (top view). TABLE I SYSTEM PARAMETER OF THE L10 WEC (14) (15) (16) (17) convolution terms can be approximated by a linear state space model by methods described in [18] and [19]. However, this is not necessary here. For model predictive control in general, using a model in state space form is convenient. Equations (1)–(8) can be reformulated as (9) (10) These equations can be transformed in state space form by sub- stituting (9) into (10) and vice versa to get rid of the coupling terms and . The equations can be restated as where the PTO force is now denoted by and the excitation forces and by and , respectively. In our work, we consider three different models. The ﬁrst model (1)–(8) with convolution terms is called the extended model. The plant model in the simulations is always this model. The second model (13)–(17) without convolution terms is called the reduced model. The nonlinear MPC approach is based on this one. The only nonlinearity in the reduced model is the mooring force. Replacing the nonlinear mooring force by the linear law (18) yields the linear model, which the linear MPC is based on in this paper. B. Discretization A ﬁnite parameterization of the controls and constraints is (11) used to ﬁnd a direct solution of the optimization problem. There- fore, the system must be described as a discrete-time nonlinear state space model in the form (19) (12) Now, the nonlinear state space model with the state vector and the initial conditions where the function maps the current state , the control- lable input , and the uncontrollable input to the next state . According to [20], a nonlinear system (20) RICHTER et al.: NONLINEAR MODEL PREDICTIVE CONTROL OF A POINT ABSORBER WAVE ENERGY CONVERTER 121 with real analytic vector ﬁelds and can be dis- cretized by an approximate sampled-data representation under zero-order hold assumption by (21) where (22) with . Here denotes the order of the discretization, and is the sampling interval. Simulation shows that a discretization of order 1 (comparable to Euler forward method) is not appropriate for the proposed NMPC approach. In fact, it yields unreasonable results. Due to this fact, the discretization order is chosen to be in the ensuing work. With the nonlinear reduced system (13)–(17), it follows that (23) (24) Using (21), after some manipulations the discrete-time system can be described by (25) where (26) (27) (28) with the Jacobian matrix, shown in (29) at the bottom of the page. III. PROBLEM FORMULATION AND IMPLEMENTATION OF NMPC Linear MPCs are well known and have been applied since the 1970s, whereas NMPC’s have been used since the 1990s [21]. Linear MPC theory is quite mature today and system theoretic attributes such as stability and optimality are well addressed [22]. Also, many different industrial MPC applications can be found. The case is different with NMPC. While theoretical characteristics are well discussed, industrial applications are difﬁcult to ﬁnd [23]. Linear MPCs and NMPCs have basically the same concepts. In general, the NMPC problem is formulated as solving a ﬁnite horizon optimal control problem which is subject to constraints and to system dynamics [21]. The readers are referred to [21] and [24] regarding general information about MPC. A fundamental problem germane to NMPC schemes is that the constrained optimization problem needs to be solved within a speciﬁed time limit. In the case of linear MPC, the problem is convex and for the class of LQP, proven optimization algorithms exist to solve the problem efﬁciently. NMPC requires the solution of a nonlinear problem, though. In general, these problems are nonconvex, thus it cannot be assured to ﬁnd the global optimum. Additionally, the solution can be computationally expensive. Therefore, it is important to exploit the special structure of each problem to obtain a real-time feasible optimization problem. This work does not focus on real-time applicability. The focus is on the qualitative performance of NMPC regarding a nonlinear WEC model whose nonlinearity results from a nonlinear mooring force. So, this work attempts to establish if NMPC is advantageous to use for controlling the selected nonlinear WEC compared to the linear MPC. The NMPC and the linear MPC are implemented using Matlab/SIMULINK. The linear MPC is based on the work in [13] and uses the reduced system with the linear mooring law (18). The NMPC uses the solver “fmincon” of Matlab which can handle nonlinear problems with nonlinear constraints as well. Here, the interior-point method is used as the optimization algorithm of “fmincon.” In the following, the solution of the nonlinear optimization problem using Matlab is outlined. The objective function is formulated to include one term expressing the generated power and another term presenting the energy use. In general, the generated power for point absorbers is the product of relative velocity and PTO force. Furthermore, the optimization problem includes slack variables and as (29) 122 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 4, NO. 1, JANUARY 2013 in [13] to avoid infeasibilities. The optimization problem can be stated as TABLE II NMPC/MPC PARAMETER VALUES (30) where (31) subject to (32) (33) (34) (35) (36) where , , and are the relative position, velocity, and the generator constraints, respectively. and are the weighting factors that penalize the slack variables and and are the constraints for them. The problem is implemented using the following optimization vector: (37) with variables. It is straightforward to express the ob- jective function by means of . The solver “fmincon” can handle box constraints, linear, and nonlinear inequality and equality constraints. The slack vari- ables (36) and the input constraints (35) are considered as box constraints which yield . The position (33) and velocity (34) limits are considered as linear inequality constraints which yield . Additionally, the system dynamics (32) need to be included as constraints, here as nonlinear equation constraints with equations. According to [10], the wave’s motion can be accurately pre- dicted for up to two typical wave periods into the future. Thus, a prediction time of at least 10 s is realistic. However, it should be noted that a large horizon time normally improves the perfor- mance, but at the same time, the computational effort increases since the optimization problem increases. Due to this fact, is chosen through simulation results. Since the results with a larger horizon time are not signiﬁcantly better, a horizon time of 3 s is used which normally contains an half wave period and thus, the dominant dynamics. In the following, s and s, and thus the horizon length is 30 steps. In summary, the optimization problem consists of 152 opti- mization variables and 304 equations for the constraints. This is a large problem for online solving of the problem within a step time of s. However, as stated above, the goal of this work is to determine the qualitive behavior of the system con- trolled by the NMPC in comparison with a linear MPC and not to discuss real-time applicability. Fig. 3. Nonlinear and linear mooring forces for different choices of . IV. RESULTS Two different controllers are proposed to control the extended nonlinear model (1)–(8): The NMPC as described in Section III and a linear MPC which is implemented as in [13]. The NMPC is based on the reduced nonlinear model without convolution terms as prediction model, whereas the linear MPC is based on the linear model with linear mooring force constant . The results with the NMPC and the linear MPC are denoted by NMPC and MPC, respectively. The simulations and control parameters are shown in Table II. In what follows, the NMPC is validated and compared to the linear MPC with different values of through simulation. Fig. 3 shows the nonlinear and linear mooring forces for cer- tain values of for spar displacements between 0.6 m. It can be seen that the nonlinear mooring law is highly nonlinear and a linearization would not work. Still, there are many possi- bilities to choose . In the following, three different cases are considered: One N m which underestimates the nonlinear mooring, another N m which assumes a large mooring force, and a third choice of so that the linear MPC generates the maximum power for the current wave data. In the beginning, a time-series from the National Data Buoy Center (NDBC) Umpqua buoy 46 229 which is deployed off the coast of Oregon north of Reedsport [25] is used as the wave data with the wave elevation . The wave is characterized by RICHTER et al.: NONLINEAR MODEL PREDICTIVE CONTROL OF A POINT ABSORBER WAVE ENERGY CONVERTER 123 Fig. 4. Comparison between the simulation results with the NMPC and the linear MPC with buoy 46 229. N m. The wave data is from the NDBC Umpqua Fig. 5. Comparison between the simulation results with the NMPC, the linear MPC with N m MPC . The wave data is from the NDBC Umpqua buoy 46 229. N m MPC , and the linear MPC with a signiﬁcant height of 1.97 m, energy period of 6.39 s, domi- nant period of 6.13 s, and a power of 12.1 kW per meter crest length. The comparison between the NMPC and the linear MPC with N m is shown in Fig. 4. First, it can be noted that the proposed NMPC yields reasonable results, the constraints are satisﬁed, and a large amount of power is gen- erated. For this choice of , the linear MPC yields its best result. Obvious differences are between 15 and 30 s, especially in the generator force and the relative position. However, these differences in the trajectories are not signiﬁcant and have no effect on the generated power, since the NMPC yields an average power of 9.45 kW and the linear MPC of 9.41 kW. More signiﬁcant differences can be seen in Fig. 5, where the results with the NMPC are compared to two different cases. The 124 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 4, NO. 1, JANUARY 2013 Fig. 6. Comparison between the simulation results with the NMPC and the linear MPC with Banks buoy 46 050. N m. The wave data is from the NDBC Stonewall linear MPC which assumes a high mooring force yields very different trajectories and worse results than the two other cases. Due to the assumption of high mooring forces, especially for small displacements, the MPC attempts to prevent the buoy’s large displacements, in order to satisfy the constraints. Since the actual mooring force is smaller, the relative motion between spar and buoy is reduced. Thus, the average generated power for this case is only 5.42 kW. The MPC with a smaller mooring constant yields clearly better results. The average generated power is 8.39 kW which is less than in the optimal case. However, this choice also has certain advantages. As can be seen, the position and generator force constraints are very well satisﬁed, namely, the trajectories do not exceed their limits. Also, the required reactive power is much less. Regardless of the values of , the simulation with NMPC produces the best power generation for this wave data. Now, the controller performance with different wave data, where the generator force limit is also increased to 80 000 N, is tested. The wave data is from the NDBC Offshore buoy 46 050 [25] with a more energetic sea state. For this case, the wave has a signiﬁcant height of 3.81 m, energy period of 9.33 s, dominant period of 10.4 s, and a power of 66.3 kW per meter crest length. The optimal value of for this wave data is 150 000 N/m. Fig. 6 shows the comparison between NMPC and the linear MPC for the new wave data, where some differences can be seen. The NMPC trajectories are well within the constraints. Thus, the entire power potential is not used. The average generated power for the NMPC is only 17.98 kW, compared to 22.8 kW for the linear MPC. Even though the parameter is perfectly chosen and there exist many values which do not yield such results, it is remarkable that the results using the linear MPC are better. However, one reason is that the velocity produced by the MPC oscillates around extreme values that exceed the constraints. Since power is the product of velocity and generator force, the power produced by the NMPC is lower, because the velocity is more often within the constraints. Nevertheless, it can be concluded that the proposed NMPC has probably certain weak points. One reason might be the discretization method of only an order of 2, whereas the linear discretization ignores physical model attributes. Furthermore, in some cases, the nonlinear solver might only ﬁnd the local optimum which yields inferior results. In future research, these problems need to be addressed. Nevertheless, the results with NMPC satisfy the constraints and are reasonable. It is interesting to see for which values of the linear MPC yields the best results and how sensitive this controller is with respect to changes of . In Fig. 7, the generated power depending on the value of for both wave data sets is shown. As stated above, the result with the NMPC is the best for the milder sea state. However, with a mooring constant of about N m, the results with the linear MPC are almost the same. Especially for higher mooring constants, the average generated power decreases signiﬁcantly. For the rougher sea state, there is a large range of values of where the results with the linear MPC are better than with the NMPC. Also, there is a considerable range of values for in both cases, where changes of do not affect the generated power very much. It can be observed that the optimum performance for the rougher sea state is achieved by a larger value of , namely, 150 000 N/m. This can be explained by the spar position. The spar position for the milder sea state is around 0.35 m, whereas it is 0.42 m for the rougher climate, since the wave exerts more force to the bodies. Due to the larger displacements, RICHTER et al.: NONLINEAR MODEL PREDICTIVE CONTROL OF A POINT ABSORBER WAVE ENERGY CONVERTER 125 Much work remains to be done to practically implement NMPC in ocean wave energy applications. The implementation of MPC and NMPC requires prediction of the wave excitation forces over the receding horizon. This work assumes an ideal prediction of the excitation forces. The usage of prediction algorithms as in [8] can enhance this work and can help to identify the inﬂuence of prediction errors. Our scheme envisages using wave riders in the periphery of the wave energy converter farm to measure incoming wave information with appropriate sensors, which is then passed to the neighboring wave energy converters via a cheap communication system. The inner wave energy converters also act as wave information sensors and proceed to pass this information to the remaining neighboring wave energy converters, in a communication network fashion. It should be noted that, at present, this NMPC approach is a concept and does not focus on real-time applicability. Online computation is not possible with the Matlab solver used in our work. Thus, much work needs to be done to design a fast problem-oriented optimizer. Also, it can be expected that the performance of the NMPC can be improved by a higher discretization order. However, this would yield a larger optimization problem, and therefore, the computational effort would increase. Fig. 7. Generated power with the linear MPC depending on compared to the generated power with the NMPC. On the top plot with wave data from buoy 46 050 and on the bottom with data from buoy 46 229. the mooring force increases signiﬁcantly because of the highly nonlinear mooring force as seen in Fig. 3. In this plot, it can be seen that with the optimal value of the linear mooring force is similar to a best-ﬁt line for the nonlinear mooring force. In this case, higher nonlinear mooring forces occur. Thus the best-ﬁt line is naturally a line with a larger slope, which results in a larger mooring constant . V. CONCLUSION This paper presented an NMPC approach for controlling a point absorber WEC. A nonlinear WEC model with highly nonlinear mooring force was controlled by the NMPC and by a linear MPC that assumed a linear mooring force. It was successfully demonstrated that the NMPC is able to maximize generated power while keeping the point absorber states and the generator force within their limits. In order to evaluate the performance, the results were compared with those obtained by a linear MPC with varying mooring constant through computer simulation for different irregular wave data. For the ﬁrst wave data, the NMPC yielded the best result, regardless of the choice of the mooring constant for the linear MPC. However, there were better results of the linear MPC for several choices of the mooring constant for the second wave data. Thus, only the nonlinear mooring force does not completely justify the use of NMPC. Nevertheless, in the case of stronger nonlinearities, this work provides a good framework for controlling point absorbers by NMPC. REFERENCES [1] A. Muetze and J. Vining, “Ocean wave energy conversion—A survey,” in Proc. 41st IAS Annual Meeting Conf. 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Scokaert, “Constrained model predictive control: Stability and optimality,” Automatica, vol. 36, no. 6, pp. 789–814, 2000. [23] T. Badgwell and S. Qin, Nonlinear Predictive Control—Theory and Practice, B. Kouvaritakis and M. Cannon, Eds. London, U.K.: Inst. Elect. Eng., 2001. [24] C. Bordons, Model Predictive Control. New York: Springer, 2003. [25] National Data Buoy Center Sep. 2011 [Online]. Available: http://www. ndbc.noaa.gov/ Mario E. Magaña (M’78–SM’94) received the B.S. degree in electrical engineering from Iowa State University in 1979, the M.S. degree in electrical engineering from the Georgia Institute of Technology in 1980, and the Ph.D. degree, also in electrical engineering, from Purdue University in 1987. He is currently an Associate Professor of Electrical Engineering and Computer Science at Oregon State University, Corvallis, OR. He was a Fulbright Professor at the National University of La Plata, Argentina and was an invited Researcher/Lecturer at the Universities of Ulm, Stuttgart, and Offenburg in Germany, and at the Technical University of Catalunya in Barcelona, Spain. Prior to joining the faculty at Oregon State University, he spent several years working in the Analysis and Technology Group of the Communications Systems Division at the Harris Corporation in Melbourne, FL, in the Flight Control Systems Research Unit at the Boeing Company in Seattle, WA, and at NASA’s Marshall Space Flight Center in Huntsville, AL. He is the author of more than 90 technical and scientiﬁc papers, and has written two books on network coding and one on structural control. His current areas of research are in the ﬁelds of mobile wireless communications, automatic control applications, and mathematical modeling of biological systems. Dr. Magaña is a NASA faculty fellow and a member of HKN, the electrical engineering honorary society. Oliver Sawodny received the diploma degree in electrical engineering from the University of Karlsruhe, Germany, in 1991, and the Ph.D. degree from the University of Ulm, Germany, in 1996. In 2002, he became a full professor at the Technical University of Ilmenau, and in 2005, he became the Director of the Institute for System Dynamics at the University of Stuttgart. His current research interests include methods of differential geometry, trajectory generation, methods and application to mechatronic systems. Markus Richter was born in Waiblingen, Germany in 1987. He received the Dipl.-Ing. degree in engineering cybernetics from the University of Stuttgart, Germany, in 2011. In 2011, he joined the Institute for System Dynamics at the University of Stuttgart as a research engineer working toward the Ph.D. degree. His research interests include modeling and identiﬁcation of mechanical systems, linear and nonlinear control theory, and industrial applications of systems engineering with emphasis on mobile harbour and offshore-cranes. Ted K. A. Brekken (M’06) received the B.S., M.S., and Ph.D. degrees from the University of Minnesota in 1999, 2002, and 2005, respectively. He studied electric vehicle motor design at Postech in Pohang, South Korea in 1999. He also studied wind turbine control at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway in 2004–2005 on a Fulbright scholarship. He is an Assistant Professor in Energy Systems at Oregon State University. His research interests include control, power electronics, and electric drives; speciﬁcally digital control techniques applied to renewable energy systems. He is codirector of the Wallace Energy Systems and Renewables Facility (WESRF). Dr. Brekken is a recipient of the NSF CAREER award and a recipient of the 2011 IEEE Power and Energy Society Outstanding Young Engineer Award.

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