Advanced Control of Marine Power System

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Presentation transcript:

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 Simulation tools Matlab/Simulink and PSS/E

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

Marine Power System 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.

IFEP Power System Description IFEP System Topology 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 GTA 20MW IM 20MW DG 2MW 2MW GTA 20MW IM 20MW DG 2MW 2MW

IFEP Power System Linearisation 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. Manoeuvre

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

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 for advanced control techniques application like Eigenstructure Assignment and Neurocontrol.

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.

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

Eigenstructure Assignment Control First introduced by Wonham 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

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

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

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

MODEL 1 CONTROLLER RESPONSE Eigenvalues Step Response Specifications Stability and Performance Requirements Met

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

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

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

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.