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Approximate Dynamic Programming and Reinforcement Learning for Nonlinear Optimal Control of Power Systems November 4, 2003 Ronald Harley Georgia Institute.

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Presentation on theme: "Approximate Dynamic Programming and Reinforcement Learning for Nonlinear Optimal Control of Power Systems November 4, 2003 Ronald Harley Georgia Institute."— Presentation transcript:

1 Approximate Dynamic Programming and Reinforcement Learning for Nonlinear Optimal Control of Power Systems November 4, 2003 Ronald Harley Georgia Institute of Technology ECS-0231632 ECS-0080764 Kumar Venayagamoorthy University of Missouri-Rolla

2 Adaptive Critic Design: Nonlinear Optimal Control Plant Informaton Utility Function ( U ) Optimal cost-to-go function ( J ) Critic Networks : To minimize the value (of derivatives) of J with respect to the states Derivatives via BP Model Network (Identifier) : To learn the dynamics of plant Model Network Action Network : To find optimal control u Plant Control Reinforcement Learning

3 STATCOM Control

4 Simulation Results 100ms SC at PCC, Line Voltage, Generator Terminal Voltage

5 The simplified schematic of the SSSC (160 MVA, 15KV V L-L ) Optimal control for FACTS devices Internal control for static series synchronous compensator (SSSC)

6 Optimal control for FACTS devices Internal control for SSSC (CONVC) PI Based internal controller (CONVC) for the SSSC Publication: N.G. Hingorani and L. Gyugyi, “Understanding FACTS-Concepts and Technology of Flexible AC Transmission Systems”, IEEE Press, New York, 2000.

7 Optimal control for FACTS devices Case study: 100 ms three phase short circuit test at receiving-end (infinite-bus) Rotor angle

8 Schematic single-line diagram showing an SCRC with external controller (160 MVA, 15KV V L-L ) Optimal control for FACTS devices External control for series capacitive reactance compensator (SCRC)

9 Optimal control for FACTS devices DHP based external controller (DHPEC) Schematic single-line diagram showing the DHP based external controller (DHPEC) Synchronous Generator Inf. bus v s i s SCRC r e2 x r e1 x Turbine- Governor AVR - Exciter Internal Control of SCRC Voltage Source Inverter V dc v c + GTO    X C + + Line #1 Line #2 * C X C X v r DHP based external controller (DHPEC)

10 Optimal control for FACTS devices Case study: Step changes X* C [pu] Speed deviation

11 Application in Multi-Machine power system Large-scale multi-machine power system

12 A UPFC in the POWER SYSTEM Infinite Bus Shunt Inverter Series Inverter VdcVdc Series Inverter Control Shunt Inverter Control V1V1 V dcref R 1, L 1 V2V2 V1V1 V 1ref Z1Z1 Synch Generator Governor AVR Exciter + - UPFC  Z1Z1 V 1ref  V dc P ref  P inj Q inj Q ref  1 2 P out, Q out V err V dcerr P err Q err R 2, L 2 VrVr Turbine P ref Neurocontroller Neuroidentifier QQ PP eded eqeq Neurocontroller Neuroidentifier  V dc VV  e pd  e pq

13 Responses of the Generator for a 180 ms 3- phase Short Circuit at bus 2 at P=0.8 p.u & Q=0.15 p.u Load angle Speed response

14 Micro-Machine Research Lab. at the University of Natal, Durban, South Africa

15 Gen. #1: Trans. Line Impedance Increase 1010.51111.51212.51313.51414.515 20 25 30 35 40 Time in seconds Load angle in degrees DHP_CONV CONV_PSS_CONV CON_CONV 1010.51111.51212.51313.51414.5 0.97 0.98 0.99 1 1.01 Time in seconds Terminal voltage in pu DHP_CONV CONV_PSS_CONV CONV_CONV


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