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

Fluid in Value Speed Control Governor w ref  

ref

w Turbine Network Measurement Generator Fluid Out Terminal Voltage Control + Exciter I f AVR V t V ref

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 GTA 20MW

4.1 KV 0.4 KV 3.3 KV

IM 20MW DG 2MW GTA 20MW

4.1 KV 2MW 3.3 KV 0.4 KV

IM 20MW DG 2MW

2MW

• Generators – two GTA – two DG.

• Propeller load – Induction machine • Services Load – lighting, computer and other ship service

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

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 Assignment and Neurocontrol.

like Eigenstructure

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   

P MECH

1 ..

4

E FD

1 ..

4   

r -

Controller IFEP system 8-outputs

y

  

N E T

1 ..

4 1 ..

4   

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

8-Inputs   

P MECH

1 ..

4

E FD

1 ..

4   

r

FSFB Control Law: U= -Kx+Pr

P u B



x



x C

8-outputs

y

  

N E T

1 ..

4 1 ..

4    EA Algorithm

A K

24-States          

E E

  

q

1

d kq

1

kd

1 1 ..

1 ..

..

4 ..

4 4 ..

4 1 ..

4 4         

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   

P MECH E

1 ..

4

FD

1 ..

4

ref ref

   Neuro Controller Error TDL IFEP System   

N

1 ..

4 

E T

1 ..

4   Desired Response Predictor TDL Error

Neuro Identifier

MODEL 1 CONTROLLER RESPONSE Eigenvalues

Output N 1 N 2 N 3 N 4 E T1 E T2 E T3 E T4 Bandwidth (rad/sec) Coupling 1 1 <10% <10% 1 1 10 10 10 10 <10% <10% <10% <10% <10% <10% 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) Inputs Controller Bank Control Signals

IFEP System

Outputs 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.