1 / 19

Advanced Control of Marine Power System

Advanced Control of Marine Power System. Raja Toqeer 21 January 2004. Introduction. Research focus is on Generator Excitation and Turbine Control. Marine Power System is chosen as a case study. Design and Analysis using different Control Methodologies. Comparison of Control Methodologies

garrison
Download Presentation

Advanced Control of Marine Power System

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Advanced Control of Marine Power System Raja Toqeer 21 January 2004

  2. Introduction • Research focus is on Generator Excitation and Turbine Control. • Marine Power System is chosen as a case study. • Design and Analysis using different Control Methodologies. • Comparison of Control Methodologies • Simulation tools Matlab/Simulink and PSS/E

  3. wref Network Speed Control Governor Measurement Terminal Voltage Control w Generator Turbine Vt + - If AVR Fluid Out Exciter Vref Generator Excitation and Turbine Control Fluid in Value

  4. Integrated Full Electric Propulsion (IFEP) is a proposed marine power system for the next generation Electric Ship. IFEP power system has economic benefits Low Running Cost (maintenance & fuel) Stand alone nature of IFEP allow the designer to address a generator control system that give maximum performance and operational benefits. Marine Power System

  5. Generators two GTA two DG. Propeller load Induction machine Services Load lighting, computer and other ship service 4.1 KV 3.3 KV 0.4 KV 4.1 KV 3.3 KV 0.4 KV IFEP Power System Description IFEP System Topology GTA 20MW IM 20MW DG 2MW 2MW GTA 20MW IM 20MW DG 2MW 2MW

  6. Power System analysis in PSS/E. Assumed that System dynamics depends upon Propellers operating conditions (OC). Manoeuvre for Propellers OC is defined. 50 Linear state space models are obtained. IFEP Power System Linearisation Manoeuvre

  7. Eigenvalues Step Response IFEP Models Analysis Open Loop System at 50 operating conditions Open Loop System is Unstable

  8. Motivation for Advanced Control • Traditionally Generator voltage and speed control is perform with AVR & Governors. • Large disturbance changes the system conditions in highly non-linear manner. • Due to this controller parameters may no longer be valid. • Also MIMO nature of IFEP system introduce Electro-Mechanical coupling. • These characteristics provide motivation foradvanced control techniques application likeEigenstructure Assignment and Neurocontrol.

  9. Conventional Controllers • Conventional Controller (CC) • Voltage is Controlled with AVR • Speed is Controlled with Governor • AVR and Governor both based upon decentralised PID controllers. • PID controllers are designed upon some optimal operating conditions (OC). • The tuning of the PID is required when OC changes to satisfy the performance requirements.

  10. 8-outputs 8-Inputs IFEP system Controller y r - Conventional Controllers Configuration

  11. Eigenstructure Assignment Control • First introduced byWonham in 1960s for linear multivariable control systems. • Eigenstructure Assignment allows the designer to satisfy Performance requirement with the choice of Eigenvalues and Eigenvectors. • Synthesis technique • Full State Feedback (FSFB) control • Output Feedback control

  12. EA Controller Configuration FSFB Control Law: U= -Kx+Pr 8-outputs 8-Inputs y u x r C P B - 24-States A K EA Algorithm

  13. NeuroController • NeuroController is a control system based upon neural network architecture. • Two Separate NeuroController for Excitation and Turbine Control • A NeuroIdentifier for power system dynamics identifications. • Trained Online Continuously

  14. NeuroController Configuration TDL Desired Response Predictor IFEP System TDL Neuro- Controller Error Neuro- TDL Identifier Error

  15. Eigenvalues Step Response MODEL 1 CONTROLLERRESPONSE Specifications Stability and Performance Requirements Met

  16. Eigenvalues Step Response MODEL 1 CONTROLLER Test Controller 1 is Tested for Model 1 to 10 Instability and Performance Degradation

  17. MULTI MODEL CONTROL STRATEGY MMC proposed to address the Performance and Robust Stability Issues Scheduling Variables (Demand Power) Controller Bank Control Signals Inputs Outputs IFEP System - Design of local Controllers at all operating conditions

  18. Eigenvalues Step Response MULTI MODEL CONTROL TEST Multi Model Control applied to Model 1 to 10 Stability and Performance Requirements Met

  19. CONCLUSION • Classical Controllers are designed upon linear models and tuning of controllers is required when Operating Conditions changes. • Eigenstructure Assignment Controllers are designed upon linear models; shows better performance then Classical Controllers but lacking in robustness. • NeuroController applied to non-linear system it adjust automatically when Operating Conditions changes.

More Related