Seminar on Energetic Macroscopic Representation

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1 Seminar on Energetic Macroscopic Representation From Modelling to Representation and Real-Time Control Implementation Philippe Barrade EPFL Laboratoire d Electronique Industrielle EPFL-STI-IEL-LEI, Station Lausanne, Switzerland philippe.barrade@epfl.ch Content Introduction Key elements on EMR Main principles on representation and inversion- based control Basic elements Conventional control and stability of input filters Input filter stabilization Simulation and experimental results Application to a hybrid elevator Representation and Inverse Based Control Considerations on strategies and tests POPS at CERN Representation and Inverse Based Control Tests on a real-time simulator Conclusion 1

2 Introduction Design of systems and their dedicated control requires efficient simulation tools Launching Matlab/Simulink is more and more a Pavlov reflex real system system simulation But: Why simulation? Which contraints and objectives? Which level of accuracy? behavior study Introduction Design of systems and their dedicated control requires efficient simulation tools Intermediary steps are required for complex systems The representation step is fundamental to underline special properties to be taken into account, depending on given objectives real system assumptions no assumption assumptions system model system representation system simulation 2

3 Introduction Energetic Macroscopic Representation (EMR) Is a graphical description Real-time control and energy management of energetic systems causal models functional description causal dynamical models & forward approach static or quasi-static models systemic (cognitive) Introduction Main objective in using EMR Define a procure to identify the control of systems Ready to be implemented for real-time control Example of HEV control Parallel HEV BAT VSI Fuel EM ICE EMR Trans. fast subsystem controls slow system supervision EM How to ICE define control Trans control scheme of control complex control systems? (algorithm? sensors?) Energy management (supervision/strategy) driver request 3

4 Key elements of EMR Main principles (1) Systemic approach: a system is made of interconnected subsystems organized for a common objective, in interaction with its environment. Input: produced by a subsystem, imposed to its close subsystem Output: consequence of the subsystem evolution, imposed to its close subsystems Interaction principle: each action induces a reaction S1 action reaction power S2 Key elements of EMR Main principles (2) Internal accumulation of energy (with or without losses) Key transformation for safety and efficiency Output(s) is an integral function of input(s), delayed from input(s) changes Causal description: fix input(s) and output(s) C i c v C model simulation v C d dt i c W c = 1 2 Cv c 2 For energetic systems physical causality is VITAL v c = 1 C Representation simulation i cdt i c risk of damage v C delay no energy disruption 4

5 Key elements of EMR Main principles (3) Internal accumulation of energy (with or without losses) Key transformation for safety and efficiency Output(s) is an integral function of input(s), delayed from input(s) changes Causal description: fix input(s) and output(s) Conversion of energy without energy accumulation (with or without losses) No delay from input(s) changes Non causal description: input(s) and output(s) can be permuted Key elements of EMR Main principles (4) The control is established from a cause to an effect, by inversion of each subsystem model Direct if the model is non causal Controler if the model is causal cause input SS 1 SS 2 SS n output measure? measure? measure? right cause C 1 C 2 C n desired effect EMR = system decomposition in basic energetic subsystems (SSs) Inversion-based control = systematic inversion of each subsystems using open-loop or closed-loop control 5

6 Key elements of EMR Elements of EMR EMR Source accumulation element coupling elements conversion element controller + disturbance rejection distribution criteria direct inversion + disturbance rejection Structure and conventional control scheme Example of a battery charger Simplified control A resistor R is needed to damp the input filter The input filter oscillations impact on the output current if not taken into account by the control of the converter 6

7 From oscillations rejection to instabilities (1) The control must enable the regulation of the input current, by rejecting the input filter oscillations Use of an Energetic Macroscopic Representation (EMR), leading to an inversion based control identification From oscillations rejection to instabilities (2) The input filter oscillations are now rejected The input filter becomes unstable The input filter oscillations still impact on the output current ripple By rejecting the input filter oscillations, the converter absorbs an average constant power: P o = U c I e This makes the converter to behave as a negative impedance which loads the input filter: 0 = I e δu c +U c δi e Z d = δu c = U c = U 2 c δi e I e P o Z d versus R defines the unstability conditions of the input filter: function of the operating point P o 7

8 Input filter stabilization 2 solutions Adapt the value of the damping resistor Stabilization by the correct control of the converter Control for the input filter stabilization Only 1 tuning parameter (D) for two objectives Regulation of the input current Stabilization of the input filter Input filter stabilization: merged control loop One defines 2 independant control loops (one per objective) 2 duty cycles are then defined D u for the input filter stabilization loop D i for the output current control The two duty cycles are merged to obtain the required duty cycle D Weightning factor k w 8

9 Input filter stabilization: modeling and sizing rules (1) Open loop average model C U c = 1 ( R U e U c) + I l DI s Duty cycles definition L I l = U e U c L s I s = DU c U so D u = I e _ ref = 1 k p U e U c I s I s ( ) + I l + 1 ( R U e U c ) D i = U s _ ref U c Input filter stabilization: modeling and sizing rules (2) Closed loop average model Linearization along a given operating point U c U c I l = A. I l + B U e U I s _ ref s I s A = k w 1 RC k w L s k w k p C + P o( 1 k w ) 2 CU e 1 L U so + U e k p 1 U e I so R 1 k w C 0 0 U e k w L s I so ( ) U so 1 k w CU e U so k w L s I so 1 1 C R k w R + k w k p 1 B = L k w U e 1 L s I so R k p U so L s U e ( ) I so 1 k w CU e 0 1 k w L s 9

Input filter stabilization: modeling and sizing rules (3) Stability analysis Considering the transfer function F s (s) = U c (s) I so (s) Focusing on its denominator D(s) = a 0 s 4 + a 1 s 3 + a 2 s

10 Input filter stabilization: modeling and sizing rules (3) Stability analysis Considering the transfer function F s (s) = U c (s) I so (s) Focusing on its denominator D(s) = a 0 s 4 + a 1 s 3 + a 2 s 2 + a 3 s + a 4 The coefficients are functions of Main parameters (L,C,R, etc ) Operating point (U e, U so, I s_ref =I so ) Dominant poles only are considered Equivalent 2 nd order system: oscillation frequency (ω n ) and damping (ζ) Input filter stabilization Defined by the weightning factor k w Defined by the input filter stabilization control loop Simple proportional controler k p I s_ref =I so =20A, U so =60V 10

7 Simulation and experimental results Experimental validation has been

11 Simulation and experimental results Results for 2 different dynamic properties: ζ=0.1 ζ=0.7 Simulation and experimental results Experimental validation has been performed at L2EP, from calculations made at LEI Topology of theinput filter is an LC filter, taking into account the coil series resistance 11

Structure and objectives Structure Application to a hybrid elevator

energetic point of view Identify all the possibilities to control the

accumulator Add a new degree of freedom It interacts with the system at the

12 Structure and objectives Structure Application to a hybrid elevator Objectives Identify how the accumulator impacts on the system from an energetic point of view Identify all the possibilities to control the system Representation Application to a hybrid elevator Insertion of an accumulator Add a new degree of freedom It interacts with the system at the same level than the braking resistors It is pure energy accumulation, offers the reversibility 12

Application to a hybrid elevator Objectives 3 mains objectives Control of the speed of the elevator Control of the charge/discharge current for the accumulator Control of the current in the braking

13 Application to a hybrid elevator Objectives 3 mains objectives Control of the speed of the elevator Control of the charge/discharge current for the accumulator Control of the current in the braking resistors Constraints 2 additional constraints Energy dissipated in braking resistor must be minimized Power fluctuations in the grid must be minimized Application to a hybrid elevator Control and Strategy DC bus voltage control Each reference current is not set directly, but from distribution elements (inversion of coupling elements) Distribution element need weight factor (k w ): strategies 13

Application to a hybrid elevator Considerations on strategies Test of different strategies for a given supercapacitive tank Rules Based stragegy Braking resistors used during energy recovering mode.

14 Application to a hybrid elevator Considerations on strategies Test of different strategies for a given supercapacitive tank Rules Based stragegy Braking resistors used during energy recovering mode. Elevator needs are covered by the Scaps only, recovering of energy is possible. Elevator needs are covered by the Scaps, a reduced power (P ref ) is taken on the grid, recovering of energy is possible. Energy reserve in case of grid black-out, Elevator needs are covered by the grid only. Application to a hybrid elevator Considerations on strategies Test of different strategies for a given supercapacitive tank Dynamic Programming Definition of a sequential energy consumption strategy (sliding window of 7 missions) state variable x k : state of charge of the accumulator decision variable u k : amount of energy taken on the grid perturbation variable w k : energy required by the elevator cost function g k, to be minimized c, h and p: weighting values. X max : maximum state-of-charge of the accumulator, m: number of elevator cycles P kw : probability of occurrence of elevator cycles. 14

15 Tests on an elevator Considered elevator Shaft length: 18m, 5 floors Cabin mass: 800kg Up to 8 passengers Accumulator capacity: 20Wh Application to a hybrid elevator Tests on an elevator Experimental results Application to a hybrid elevator 15

novel 60 MW Pulsed Power System based on Capacitive Energy Storage for Particle Accelerators.», C. Fahrni, A.

16 Test on strategies Experimental results Application to a hybrid elevator POPS at CERN From C. Fahrni (PhD, 2008) Simplified structure, as implemented in a reduced-scale prototype at LEI Datas extracted from: «A novel 60 MW Pulsed Power System based on Capacitive Energy Storage for Particle Accelerators.», C. Fahrni, A. Rufer, F. Bordry and J. P. Burnet, in EPE Journal : European Power Electronics and Drives Association Journal, vol. 18, num. 4, p. 5-13,

Main parameters Converters (10kHz switching frequency) AC/DC: 3 phases VSI DC/DC: 2 quadrants, voltage reversible Coil: L=4.3H, R=1.5Ω Capacitors C 1 =68mF, C 2 =0.8C 1, C 3 =1.

17 Main parameters Converters (10kHz switching frequency) AC/DC: 3 phases VSI DC/DC: 2 quadrants, voltage reversible Coil: L=4.3H, R=1.5Ω Capacitors C 1 =68mF, C 2 =0.8C 1, C 3 =1.1C 1 Nominal Voltage: 45V POPS at CERN EMR and inverse-based control POPS at CERN 1 voltage + 1 current controler are required for the link to the grid 1 current controler is required for the current in the coil Voltage equalization on the capacitors is not control, just strategy! 17

POPS at CERN From representation to control implementation The system is modelled using a real-time simulator (Typhoon, HIL600) Control is implemented using the

1ms), just a pure translation from control scheme into C++ code Strategy is implemented in a second interruption (10ms), defines the weighting factors required for the

18 POPS at CERN From representation to control implementation The system is modelled using a real-time simulator (Typhoon, HIL600) Control is implemented using the standart control platform at LEI (imperix SA, Boombox) Control is implemented in the main interruption (0.1ms), just a pure translation from control scheme into C++ code Strategy is implemented in a second interruption (10ms), defines the weighting factors required for the capacitors voltage equalization POPS at CERN Main results for the pre-charge of capacitors Initial conditions C1 is precharged, C2 and C3 are fully discharged «small cycles» in the coil (2A), to set all voltages at their nominal values 18

19 Main results for the normal cycles (6A) Strategy POPS at CERN K=0.05 K=0.2 Main results for the normal cycles (6A) Grid interface POPS at CERN 19

20 Conclusion EMR and IBC are powerful tools for representing a system and identifying some possible controls Once an IBC has been establish The most difficult part of the implementation is the identification of the correct strategy There is a strong link between the strategies and the physical system itself EMR and IBC are not only tools dedicated for the control of systems EMR and IBC are also open doors for an optimal sizing of the system itself Parameters of the strategy Power from the grid P ref Voltage limits for the supercapacitive accumulator Strategy System itself For the same strategies, the supercapacitive tank can be re-size System sizing References A. Bouscayrol, G. Dauphin-Tanguy, R Schoenfeld, A. Pennamen, X. Guillaud, G.-H. Geitner, "Different energetic descriptions for electromechanical systems", EPE'05, Dresden (Germany), September (common paper of L2EP, LAGIS and University Dresden). C. C. Chan, A. Bouscayrol, K. Chen, Electric, Hybrid and Fuel Cell Vehicles: Architectures and Modeling", IEEE transactions on Vehicular Technology, vol. 59, no. 2, February 2010, pp (common paper of L2EP Lille and Honk-Kong University). A. Bouscayrol, M. Pietrzak-David, P. Delarue, R. Peña-Eguiluz, P. E. Vidal, X. Kestelyn, Weighted control of traction drives with parallel-connected AC machines, IEEE Transactions on Industrial Electronics, December 2006, vol. 53, no. 6, p (common paper of L2EP Lille and LEEI Toulouse). K. Chen, A. Bouscayrol, W. Lhomme, "Energetic Macroscopic Representation and Inversion-based control: Application to an Electric Vehicle with an electrical differential, Journal of Asian Electric Vehicles, Vol. 6, no.1, June issue, 2008, pp , E. Bilbao, I. Etxeberria, I. Gil and A. Rufer, «Energetic Macroscopic Representation of a Hybrid Elevator Considerations on Strategies for Energy Management», EPE 2013 : 15th European Conference on Power Electronics and Applications, Lille, France, 3-5 September 2013., A. Bouscayrol and P. Delarue, «An Energetic Based Method Leading to Merged Control Loops for the Stability of Input Filters», IEEE VPPC 2010 : Vehicle Power and Propulsion Conference, Lille, France, 1-3 September EMR website: 20

«SYSTEM, ENERGY AND CAUSALITY»

EMR 17 Lille June 2017 Summer School EMR 17 Energetic Macroscopic Representation «SYSTEM, ENERGY AND CAUSALITY» Prof. Alain BOUSCAYROL (L2EP, University Lille1, France) Prof. C.C. CHAN (University of Hong-Kong,