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MIMO Control of Three Stage Solid State Transformer

MIMO Control of Three Stage Solid State Transformer. ASU Team: Lloyd Breazeale , Youyuan Jiang, Raja Ayyanar. Motivation Average model plant derivation and validation Equilibrium and linearization Linear Quadratic Gaussian (LQG) with Loop Transfer Recovery (LTR)

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MIMO Control of Three Stage Solid State Transformer

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  1. MIMO Control of Three Stage Solid State Transformer ASU Team: Lloyd Breazeale, Youyuan Jiang, Raja Ayyanar

  2. Motivation • Average model plant derivation and validation • Equilibrium and linearization • Linear Quadratic Gaussian (LQG) with Loop Transfer Recovery (LTR) • Validation with simulation • Conclusion

  3. Motivation • Possibly original application (publication!) • Control design as a whole • Cross coupling included • Elimination of cascaded control loops • Interaction of loops not a problem

  4. The power circuit Average model equation:

  5. Real and imaginary AC circuits

  6. Rotating frame state equation Outputs State variables: Inputs: Control variables Output disturbance Input disturbance

  7. Linearization conditions • Unity power factor at input and output • Constant DC link voltages • Fixed real power • Nominal output voltage

  8. Linear system Equilibrium unknowns solved with Newton’s method resulting in equilibrium state and input vectors: With disturbance inputs set to zero, the linear system utilized in control design is as follows: where

  9. Proposed control structure

  10. One LQG/LTR design procedure [1] • Form design plant with internal model • Determine target open loop response • Solve for Kalman-Bucy filter gain • Solve LQR gain • Combine LQR and KF • Include internal model [1] A. A. Rodriguez, “Design of Multivariable Control Systems,” CONTROL3D, Tempe AZ.

  11. Design plant with integrator bank absorbed from left

  12. Bandwidth selection 10 Hz to absorb 120 Hz ripple currents 100 Hz for everything else 500 Hz to pass 120 Hz Currents across the transformer

  13. Kalman-Bucy filter as target open loop • Apply loop shaping technique • Select bandwidth vector • Use R and Q to solve Filter Algebraic Riccati Equation to get Kalman-Bucy filter gain matrix (H) where

  14. Linear Quadratic Regulator Solve Control Algebraic Riccati equation with very small penalty on control signals: This results in the LQR gain matrix (G)

  15. Complete LQG with internal model integrators absorbed from right A 24th order system

  16. Target open loop compared with actual

  17. Simulation circuit

  18. Simulation circuit

  19. Error vector LV DC reference step HV DC reference step Large load step

  20. Conclusion • Solve load step issue • Discrete time conversion and test with RTDS/HIL • Controller order reduction • Extended to include P,Q reference and energy storage

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