Modeling and control of renewable energy systems

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1 Modeling and control of renewable energy systems Christoph Hackl Technische Universität München (TUM) Munich School of Engineering (MSE) Research group Control of Renewable Energy Systems (CRES) IEEE Joint IAS/PELS/IES Meeting Munich, Germany

2 Outline 1 Introduction 2 Small-scale wind turbine systems with reluctance synchronous machines 3 Conclusion C. Hackl Modeling and control of renewable energy systems 2/29

3 Outline 1 Introduction Munich School of Engineering (MSE) MSE Research group Control of Renewable Energy Systems (CRES) Team Research Laboratory setup C. Hackl Modeling and control of renewable energy systems 2/29

4 Munich School of Engineering (MSE) Integrative research center C. Hackl Modeling and control of renewable energy systems 3/29

5 Group Control of renewable energy systems (CRES) Team H. Eldeeb, M.Sc. C. Dirscherl, M.Sc. C. Hackl, Dr.-Ing. J. Kullick, M.Sc. K. Schechner, M.Sc. Z. Zhang, M.Sc. (CRES,9/215) (CRES,1/214) (CRES,1/214) (CRES,1/215) (CRES,1/214) (CRES/EAL,7/215) External PhD candidates... and collaborations with EAL (Prof. Kennel) S. Krüner, M.Sc. A. Birda, M.Sc. F. Bauer, M.Sc. M. Abdelrahem, A. Ayad, (SINNPower,11/215) (BMW,4/216) (EAL) M.Sc. (EAL) M.Sc. (EAL) C. Hackl Modeling and control of renewable energy systems 4/29

Airborne wind energy systems Small-scale wind

converters (SINN Power) Focus: Fault-tolerant

RSMs (ď 5 kw) Electric vehicles (BMW) Focus:

control Focus: Optimal control C. Hackl 21.4.

6 Group Control of renewable energy systems (CRES) Research projects Classical wind turbine systems Airborne wind energy systems Small-scale wind turbines Focus: Efficiency and reliability Wave converters (SINN Power) Focus: Fault-tolerant control Geothermal power systems Focus: Use of RSMs (ď 5 kw) Electric vehicles (BMW) Focus: Efficiency and reliability Focus: Fault-tolerant control Focus: Optimal control C. Hackl Modeling and control of renewable energy systems 5/29

7 Group Control of renewable energy systems (CRES) Point of view and publications: Electrical components of e.g. large-scale wind turbine systems Turbine Generator Gear Generator current control Back-to-back converter Filter PCC Trafo Grid Grid current control parameter-free [7] model-based [1, 8, 13] DC-link control [1, 16 19] RL filter [1, 16, 17] LCL filter [2, 21] Grid faults MTPA, MTPV [14, 15] [2, 21] Impacts of non-ideal torque control [3, 12] Speed control encoderless [6, 9] parameter-free [1, 11] ω β m DFIG i abc s VSI AC i abc r DC s abc m DC-link u dc Machine topologies Online parameter Power control [1, 22] PMSG [1, 3, 4] estimation DFIG [5] DFIG [2, 5, 6] Control Converter topologies RSM [7, 8] Two-level converters [1] β ref ω m,ref u dc,ref Three-level NPC converters p pcc [22] q pcc Dynamic power flow [1, 2] Further contributions Operation management Dynamic friction modeling (chapter) [23] Non-identifier based adaptive control Modeling v& w control WTS (chapter) [1] in mechatronics p (monograph) pcc,ref q [24] pcc,ref Airborne wind energy (chapters) [19, 25, 26] VSI DC AC s abc g i abc f u abc g Grid synchronization PLL [1] geberlos [22] C. Hackl Modeling and control of renewable energy systems 6/29

and VSIs Electrically-excited SM and doubly-fed IM C.

8 Group Control of renewable energy systems (CRES) Laboratory setup Reluctance and Permanent-magnet SMs Real-time system and VSIs Electrically-excited SM and doubly-fed IM C. Hackl Modeling and control of renewable energy systems 7/29

9 Outline 2 Small-scale wind turbine systems with reluctance synchronous machines Motivation Preparatory work Generic model of RSM Current PI-funnel control with anti-windup for RSMs (model-free [7]) PI-funnel control with anti-windup Implementation and results Nonlinear PI current control of RSMs (model-based [8, 13, 14]) Nonlinear PI controller design Measurement results Maximum torque per Ampere (MTPA) Overall simulation C. Hackl Modeling and control of renewable energy systems 7/29

10 Motivation International cooperation with the University of Stellenbosch (Prof. M. Kamper), South Africa Goal: Replace generator with RSM C. Hackl Modeling and control of renewable energy systems 8/29

Motivation Reluctance synchronous machine (RSM): A viable alternative? http://www.abb.

11 Motivation Reluctance synchronous machine (RSM): A viable alternative? advantages [27 29]: (very) cheap high efficiency feasible (IE4) high power density and torque-per-volume ratio sensorless control feasible disadvantages: power factor highly nonlinear inverter necessary (complex/nonlinear control necessary) C. Hackl Modeling and control of renewable energy systems 9/29

12 Preparatory work Modeling, control and simulation of small-scale wind turbine system with RSM [14] current control (generator-side) maximum torque per Ampere (MTPA) speed control (maximum power point tracking (MPPT)) current control (grid-side) DC-link voltage control reactive power (feedforward) control C. Hackl Modeling and control of renewable energy systems 1/29

13 Generic model of RSM Reluctance synchronous machine with nonlinear flux linkage (without iron losses) j u k s ptq R s i k 1 s ptq ` d dt ψk s `ik s ptq ` ω k ptq ψ k looomooon 1 s `ik s ptq, m m ptq 3 :J 2 p ik s ptq J Jψ k s `ik s ptq, /. /- (1) ψ d s / Wb i q s / A 5 5 B (a) d-flux linkage (1kW RSM) i d s / A 5 ψ q s / Wb i q s / A B 5 5 (b) q-flux linkage (1kW RSM) 5 i d s / A ñ Nonlinear dynamical system! C. Hackl Modeling and control of renewable energy systems 11/29

14 Generic model of RSM Nonlinear differential inductances L d s : Bψd s, L q Bi d s : Bψq s Bi q, L dq s : Bψd s s s Bi q s Bψq s Bi d s : L qd s L d s / H i q s / A i d s / A L q s / H.2 5 i q s / A i d s / A L dq s / H L qd s / H i q s / A i d s / A 5 i q s / A i d s / A C. Hackl Modeling and control of renewable energy systems 12/29

15 Current PI-funnel control with anti-windup for RSMs Tracking with prescribed transient accuracy (model-free: no system parameters required!) Λpq epq λ Λptq eptq performance funnel F Λ Λp q BF Λ p q ep q t time t / s Controller (applicable if feasibility condition pu ě pu feas holds, see [7, Th. IV.2]): u fc ptq κptqeptq ùñ eptq ă Λptq (FC k i ) s where eptq pe d sptq, e q sptqq J i k s,refptq i k s ptq 1 κptq Λptq eptq ě 1 and eptq Λptq b e d sptq 2 ` e q sptq 2 C. Hackl Modeling and control of renewable energy systems 13/29

16 Current PI-funnel control with anti-windup for RSMs PI-funnel control with anti-windup k p sat pu e (FCi k ) s u fc u k s,ref u k s k i 9ξ ξ f pu awp q 1 d dt ξptq k i faw pu ξptq ` k p u fc ptq u fc ptq, ξpq ξ P R n u k s,refptq ξptq ` k p u fc ptq, where k p ą, k i ě. ùñ ξp q acts as bounded input disturbance (see [7, Lem. III.1]) ùñ PI-like extension for steady state accuracy + (PI aw ) C. Hackl Modeling and control of renewable energy systems 14/29

17 Current PI-funnel control with anti-windup for RSMs Control-loop and implementation (9.6kW RSM) i k s,ref PI-funnel controller u k s,ref Park trafo. dq u s s,ref Clarke trafo. αβ u abc s,ref SVM VSI power elect. u abc s nonlinear SM stator rotor ω m αβ abc u dc φ k ş pω m dt ` φ k pq i k s dq i s s αβ i abc s pi a s,i b s,i c s q J controller implementation αβ abc real world C. Hackl Modeling and control of renewable energy systems 15/29

18 Current PI-funnel control with anti-windup for RSMs Measurement results for 9,6 kw RSM e / A κ / V A i d s / A i d s,ref Λ i q s / A 2 i q s,ref 2 u k s,ref / V 4 2 û ωk / rad s 2 2 npωm,rated time t / s C. Hackl Modeling and control of renewable energy systems 16/29

19 Current PI-funnel control with anti-windup for RSMs Measurement results for 9,6 kw RSM (Zoom) e / A κ / V A i d s / A Λ i d s,ref i q s / A 2 i q s,ref 2 u k s,ref / V 4 2 û ωk / rad s time t / s npωm,rated C. Hackl Modeling and control of renewable energy systems 17/29

20 Nonlinear PI current control of RSMs (model-based) Control loop (with inverter approximation) and PI controller design u k s,comp u k s,dist V R pi k s q T n pi k s q 1 T dead 1 R s T s pi k s q i k s,ref u k s,pi u k s,ref u k s i k s where for the nonlinear case we have [8, 14] T s T s pl dd s T dead 9f pwm. pi k s q, L dq s pi k s q, L qd s u k s,comp u k s,dist (ideal decoupling) pi k s q, L qq pi k s qq Controller design according to Magnitude Optimum [3]: s Gain: V R pi k s q T spi k s qr s 2T dead rωs ^ Time constant: T n pi k s q T s pi k s q rss C. Hackl Modeling and control of renewable energy systems 18/29

21 Nonlinear PI current control of RSMs Measurement results for 1,1 kw RSM i d s [A] i q s [A] 3 i d s,ref i q s,ref 5 m m,ref [Nm] 5 ω m [rad/s] time t [s] C. Hackl Modeling and control of renewable energy systems 19/29

22 Nonlinear PI current control of RSM Measurement results for 1,1 kw RSM (Zoom) i d s [A] i q s [A] i q s,ref 2 1 i d s,ref m m,ref [Nm] 4 2 ω m [rad/s] time t [s] C. Hackl Modeling and control of renewable energy systems 2/29

23 Maximum Torque per Ampere (MTPA) Nonlinear machine torque m m rnms i q s ras i d s ras 2 4 m m ptq 3 2 p ψk s `ik s ptq J Ji k s ptq 3 2 p ψs d pi k s ptqqi q sptq ψs q pi k s ptqqi d s ptq (2) C. Hackl Modeling and control of renewable energy systems 21/29

24 Maximum Torque per Ampere (MTPA) Torque (feedforward) control: Block diagram Goal: Current reference generation m m,ref Ñ i k s,ref Constraint: High efficiency ùñ minimize copper losses 3 R 2 s i k s 2 3 R d 2 s pi s q 2 ` pi q s q 2 Strategy: Maximum Torque per Ampere (MTPA; numerical solution) MTPA m m,ref i k s,ref β s q i q s,ref i k s,ref i k s,ref (2) (a) Torque feedforward control β s i d s,ref (b) Current vector d C. Hackl Modeling and control of renewable energy systems 22/29

25 Overall simulation Small-scale wind turbine system with reluctance synchronous machine (region II) gear back-to-back converter PCC turbine generator filter trafo grid ω t ω m current control (generator side) i abc s u abc s,ref s abc m PWM s abc n PWM u abc f,ref i abc f current control (grid side) u abc g PLL û g, ω g ω m control m m,ref i k s,ref torque control u dc i d f,ref DC-link control i q f,ref q pcc control λ MTPA u dc,ref q pcc,ref C. Hackl Modeling and control of renewable energy systems 23/29

26 Overall simulation Simulation results for overall small-scale wind turbine system (region II) active power [kw] 2 p w = 1 2 ρπr2 tv 3 w p m = ω mm m p pcc 1 reactive power [kvar] 1 q pcc,ref q pcc 1 1 tip speed ratio [1] angular speed [rad/s] torque [Nm] ω m,ref λ λ ω m m m,ref m m time t [s] C. Hackl Modeling and control of renewable energy systems 24/29

27 Outline 3 Conclusion C. Hackl Modeling and control of renewable energy systems 24/29

28 Conclusion Control is essential (in particular: nonlinear control ðñ state space analysis) improves efficiency guarantees optimal operation (e.g. for different wind speeds) protects system (combined with operation management) Future challenges for Renewable Energy Systems (see also [31]) Reduction of levelized costs of Energy ùñ Energiewende economically feasible? Improving reliability and increasing fault tolerance (in particular, controls and electrical sub-system are fault-prone) Flexible and robust grid connection Emerging technologies (e.g. new generator / converter topologies) C. Hackl Modeling and control of renewable energy systems 25/29

29 References I [1] Christian Dirscherl, Christoph Hackl, and Korbinian Schechner. Modellierung und Regelung von modernen Windkraftanlagen: Eine Einführung (available at the authors upon request). In Dierk Schröder, editor, Elektrische Antriebe Regelung von Antriebssystemen, chapter 24, pages Springer-Verlag, 215. [2] Christian Dirscherl and Christoph M. Hackl. Dynamic power flow in wind turbine systems with doubly-fed induction generator. In Proceedings of the 216 IEEE International Energy Conference, 216. [3] Christoph M. Hackl and Korbinian Schechner. Non-ideal torque control of wind turbine systems: Impacts on annual energy production. Technical report, arxiv: v1 [cs.sy], 216. [4] Zhenbin Zhang, Christoph Hackl, Tongjing Sun, and Ralph Kennel. Computationally efficient DMPC for three-level NPC back-to-back converters in wind turbine systems with PMSG. submitted to IEEE Transactions on Power Electronics, 215. [5] Mohamed Abdelrahem, Christoph M. Hackl, and Ralph Kennel. Application of extended Kalman filter to parameter estimation of doubly-fed induction generators in variable-speed wind turbine systems. In Proceedings of the 5th International Conference on Clean Electrical Power, pages , 215. [6] Mohamed Abdelrahem, Christoph M. Hackl, and Ralph Kennel. Sensorless control of doubly-fed induction generators in variable-speed wind turbine systems. In Proceedings of the 5th International Conference on Clean Electrical Power, pages , 215. [7] Christoph M. Hackl. Current PI-funnel control with anti-windup for synchronous machines. In Proceedings of the 54th IEEE Conference on Decision and Control, pages , 215. [8] Christoph M. Hackl, Maarten J. Kamper, Julian Kullick, and Joshua Mitchell. Nonlinear PI current control of reluctance synchronous machines. arxiv.org/pdf/ v1, 215. C. Hackl Modeling and control of renewable energy systems 26/29

30 References II [9] Z. Zhang, C. Hackl, F. Wang, Z. Chen, and R. Kennel. Encoderless model predictive control of back-to-back converter direct-drive permanent-magnet synchronous generator wind turbine systems. In Proceedings of 15th European Conference on Power Electronics and Applications, pages 1 1, 213. [1] Christoph M. Hackl. Funnel control for wind turbine systems. In Proceedings of the 214 IEEE International Conference on Control Applications, pages , 214. [11] Christoph M. Hackl. Speed funnel control with disturbance observer for wind turbine systems with elastic shaft. In Proceedings of the 54th IEEE Conference on Decision and Control, pages , 215. [12] Christoph M. Hackl and Korbinian Schechner. Non-ideal torque control in wind turbine systems: Causes and impacts. In Proceedings of the 5 th MSE-Kolloquium on Innovations for Energy Systems, Mobility, Buildings and Materials, 215. [13] Christoph M. Hackl, Maarten J. Kamper, Julian Kullick, and Joshua Mitchell. Current control of reluctance synchronous machines with online adjustment of the controller parameters. In to be published in Proceedings of the 216 IEEE International Symposium on Industrial Electronics (ISIE 216), 216. [14] Julian Kullick. Simulation and control of a small-scale wind turbine system with reluctance synchronous generator. Master s thesis, Technische Universität München, MSE Research Group "Control of renewable energy systems", 215. [15] Lorenz Horlbeck, Markus Lienkamp, and Christoph Hackl. Analytical solution for the MTPV hyperbola including the stator resistance. In Proceedings of the IEEE International Conference on Industrial Technology (submitted), 216. C. Hackl Modeling and control of renewable energy systems 27/29

31 References III [16] Christian Dirscherl, Christoph Hackl, and Korbinian Schechner. Explicit model predictive control with disturbance observer for grid-connected voltage source power converters. In Proceedings of the 215 IEEE International Conference on Industrial Technology, pages , 215. [17] Christian Dirscherl, Christoph M. Hackl, and Korbinian Schechner. Pole-placement based nonlinear state-feedback control of the DC-link voltage in grid-connected voltage source power converters: A preliminary study. In Proceedings of the 215 IEEE International Conference on Control Applications, pages , 215. [18] Florian Bauer, Christoph M. Hackl, and Korbinian Schechner. DC-link control for airborne wind energy systems during pumping mode. In Proceedings of the Airborne Wind Energy Conference 215, 215. [19] Korbinian Schechner, Florian Bauer, and Christoph M. Hackl. Nonlinear dc-link pi control for airborne wind energy systems during pumping mode. In Roland Schmehl, editor, Airborne Wind Energy: Advances in Technology Development and Research. Springer-Verlag, 216. [2] Christoph M. Hackl. MPC with analytical solution and integral error feedback for LTI MIMO systems and its application to current control of grid-connected power converters with LCL-filter. In Proceedings of the 215 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), pages 61 66, 215. [21] Christian Dirscherl, Josef Fessler, Christoph M. Hackl, and Hanko Ipach. State-feedback controller and observer design for grid-connected voltage source power converters with LCL-filter. In Proceedings of the 215 IEEE International Conference on Control Applications, pages , 215. [22] Zhenbin Zhang, Hue Xu, M. Xue, Z. Chen, T. Sun, Ralph Kennel, and Christoph Hackl. Predictive control with novel virtual flux estimation for back-to-back power converters. IEEE Transactions on Industrial Electronics, 62(5): , 215. C. Hackl Modeling and control of renewable energy systems 28/29

32 References IV [23] Christoph M. Hackl. Dynamische Reibungsmodellierung: Das Lund-Grenoble (LuGre) Reibmodell (available at the authors upon request). In Dierk Schröder, editor, Elektrische Antriebe Regelung von Antriebssystemen, chapter 25, pages Springer-Verlag, 215. [24] Christoph M. Hackl. Non-identifier based adaptive control in mechatronics: Theory and Application. Lecture Notes in Control and Information Sciences. Springer-Verlag, Berlin, 215 (accepted; final version in preparation). [25] Florian Bauer, Christoph M. Hackl, Keyue Smedley, and Ralph Kennel. On multicopter-based launching and landing of lift power kites. In Roland Schmehl, editor, Airborne Wind Energy: Advances in Technology Development and Research. Springer-Verlag, 216. [26] Florian Bauer, Christoph M. Hackl, Keyue Smedley, and Ralph Kennel. Crosswind kite power with tower. In Roland Schmehl, editor, Airborne Wind Energy: Advances in Technology Development and Research. Springer-Verlag, 216. [27] Marten J. Kamper, F.S. van der Merwe, and S. Williamson. Direct finite element design optimisation of the cageless reluctance synchronous machine. IEEE Transactions on Power Conversion, 11(3): , [28] Thomas A. Lipo. Synchronous reluctance machines A viable alternative for AC drives? Electric Machines & Power Systems, 19(6): , [29] A. Vagati. The synchronous reluctance solution: A new alternative in AC drives. In Proceedings of the 2th International Conference on Industrial Electronics, Control and Instrumentation, pages 1 13, [3] Dierk Schröder. Elektrische Antriebe - Regelung von Antriebssystemen (3., bearb. Auflage). Springer-Verlag, Berlin, 29. [31] Frede Blaabjerg and Ke Ma. Future on power electronics for wind turbine systems. IEEE Journal of Emerging and Selected Topics in Power Electronics, 1(3), 213. C. Hackl Modeling and control of renewable energy systems 29/29

Nonlinear control of renewable energy systems

Nonlinear control of renewable energy systems Christoph Hackl Technische Universität München (TUM) Munich School of Engineering (MSE) Research group Control of Renewable Energy Systems (CRES) www.cres.mse.tum.de