1. Computational Tools for Early Stage Ship Design
2008 ESRDC Team Meeting, May, Austin, TX
Computational Tools for Early Stage Ship Design
Center for Advanced Power Systems
Florida State University
2000 Levy Avenue, Tallahassee, FL 32310

2. 2008 ESRDC Team Meeting, 20-21 May, Austin, TX
Outline
• Challenges & Goals
• Facilities & Capabilities
- CAPS Development
- High-End Tools
2008 ESRDC Team Meeting, May, Austin, TX

3. 2008 ESRDC Team Meeting, 20-21 May, Austin, TX
Challenges & Goals
Challenges
- Increased complexity and integration of all-electric ship subsystems
- Uncertainty in requirements to ship system architecture
- Uncertainty in component/subsystem characteristics, behavior, interaction
- New concepts and technologies
Modeling and Simulation at early stage of ship design is vital
Goals
• Develop and validate
- New models for components/subsystems behavior and interaction
New approaches & algorithms to enhance performance of computational tools
- New ship system architectures
• Real-time simulation
2008 ESRDC Team Meeting, May, Austin, TX

4. Facilities &Capabilities
• CAPS Models, Methodologies & Algorithms
• High-Performance Real Time Digital Simulator
• Virtual Test Bed
• Hardware-in-the-Loop
• PC-based Software
MATLAB/Simulink, PSCAD, PSIM etc.
2008 ESRDC Team Meeting, May, Austin, TX

5. 2008 ESRDC Team Meeting, 20-21 May, Austin, TX
CAPS Development
• Real-Time Particle Swarm Optimization
for PMSM Parameter Identification
• Neural Network Controller Design
• Parametric Sensitivity & Uncertainty Analysis
• Survivability Analysis for Power Systems
• Structural Analysis for Automated
Fault Detection & Isolation
2008 ESRDC Team Meeting, May, Austin, TX

6. Real-Time Particle Swarm Optimization
for PMSM Parameter Identification
Wenxin Liu, Li Liu, and David A. Cartes
Motivation
Previous PSO applications were offline solutions due to time requirements
for evaluating candidate solutions
Online implementation of PSO will result in more efficient and accurate
parameter identification
Objectives
Develop approaches/algorithms to conduct faster-than-real-time simulations
Implement PSO in real time using a hardware controller
Investigate its performance for parameter identification of PMSM
2008 ESRDC Team Meeting, May, Austin, TX

7. Comparison between measured
Real-Time Particle Swarm Optimization
for PMSM Parameter Identification
Achievements & Considerations
Developed a method to conduct faster-
than-real-time PSO-based simulations
Implemented the PSO algorithm in
Simulink using Simulink modules &
Matlab Embedded Functions
Successfully identified two parameters
in a PMSM model
Comparison between measured
& simulated data
Future Research
• Consider other approaches such as the method of direct integration
(dimension adaptive collocation) in collaboration with Dr. Hover (MIT)
Use properly simplified models to further speed up simulations
Extend the approach to other online identification, optimization,
and control problems
2008 ESRDC Team Meeting, May, Austin, TX

8. Neural Network Controller Design for 3-Ф
PWM AC/DC Voltage Source Converters
Wenxin Liu, Li Liu, and David A. Cartes
Motivation
Most controllers in power electronics are designed based on simplified linear models, which limit their performances to certain configuration and operating range
Existing nonlinear controller designs usually have trouble handling parameter impreciseness and parameter drifting
Objectives
Design a novel intelligent controller based on a nonlinear model
Approximate parameters of the system using neural network to
obtain a robust system
Achieve both unity power factor and regulated output DC voltage
2008 ESRDC Team Meeting, May, Austin, TX

9. Neural Network Controller Design for 3-Ф
PWM AC/DC Voltage Source Converters
Approach
NN based MIMO Control to
regulate Id & Iq indirectly to
realize control objectives
PI control to speed up the
convergence of zero dynamics
and generate reference signal
for the NN control
Structure of the adaptive NN controller
Achievements
Future Research
Introduced a novel NN-based
adaptive nonlinear controller design
Tested the control algorithm using
Simulink and PSIM
Tested the algorithm using dSPACE,
RTDS-based Hardware-In-The-Loop
Design path-following type of control
to control [iq, vo]  [0 ,Vo*] directly
Design a new algorithm to overcome
the unstable zero dynamics and
stability analysis problems
Consider other control problems
2008 ESRDC Team Meeting, May, Austin, TX

10. Parametric Sensitivity & Uncertainty Analysis
J. Langston, A. Martin, M. Steurer, S. Poroseva / J. Taylor, F. Hover (MIT)
Motivation: need to quantify uncertainty in results of simulation due to
Environmental (random) variables (e.g. load)
Sensitivity of simulation results to artificial parameters (e.g. time-step size)
Model (unknown) parameters (confidence bounds) (e.g. machine data)
Objectives
• From small number of evaluations of computationally expensive,
physics-based model, develop empirical surrogate models describing
system behavior as a function of model parameters
• Apply sensitivity and uncertainty analysis to computationally
inexpensive surrogate models
2008 ESRDC Team Meeting, May, Austin, TX

11. Parametric Sensitivity & Uncertainty Analysis
Surrogate Models
Polynomial models
Gaussian Process models
Additive models
Sampling Approaches
Classical experimental designs
Orthogonal arrays
Prediction variance based designs
Quadrature integration techniques
Achievements
Constructed surrogate models involving from 6 to 27 parameters
Performed sensitivity & uncertainty analysis for various models,assessed propagation of effects of a pulse load charging event
Future Research
Uncertainty in surrogate models
Uncertainty in distributions of parameters
2008 ESRDC Team Meeting, May, Austin, TX

12. Survivability Analysis for Power Systems
S. V. Poroseva, S. L. Woodruff/N. Lay, M. Y. Hussaini (SCS, FSU)
Survivability is the system ability to accomplish mission in spite of multiple
faults caused by adverse conditions (combat damage, software failure etc.)
Motivation
Survivability of Integrated Power System is vital for ship survivability
Integrated Power System Control, Propulsion, Combat, Service Loads
Mission failure
Power Loss Personnel loss
Ship destruction
Objectives
• Mathematical framework to assess system survivability
• Numerical algorithms to calculate survivability of large power systems
New system architectures of enhanced survivability
2008 ESRDC Team Meeting, May, Austin, TX

13. Survivability Analysis for Power Systems
Current focus: Topological survivability, which is due to the system topology -a number of generators, their connections with one another and loads
Achievements
• Developed the probabilistic description of topological survivability
• Assessed survivability of topologies including generators
• Developed a graph-based algorithm
• Compared design strategies (redundancy, link partition & position)
• Suggested a new topology based on bio-prototype (patent pending)
• Conducted dynamic simulation for a new generator bus of 2 generators
Future research
Bio-prototype
Web
• Susceptibility
• Larger-system algorithms
• Dynamic simulation for full system
• Fault detection & isolation
2008 ESRDC Team Meeting, May, Austin, TX

14. Structural Analysis for Automated Fault Detection & Isolation
D. Düştegör, S. V. Poroseva, S. L. Woodruff /M. Y. Hussaini (SCS, FSU)
Motivation: automated wide-area FDI methodology is required to address
current Navy demands of reduced manpower, system survivability, reliability,
availability, effective and efficient protection and control
Objectives
• Without a detailed power system model (in early stage of ship design):
assess a given system topology with respect to
- Fault Detectability
- Fault Isolability
- Extra sensor placement
• With a detailed analytical model
- Residual generator
2008 ESRDC Team Meeting, May, Austin, TX

15. Structural Analysis for Automated Fault Detection & Isolation
Approach / Methodology
• Structural model: only the relation between
variables and equations are investigated
• Canonical decomposition: yields the
“structurally” monitorable part of the system
• Matching: investigates how to eliminate
state variables and generate residuals
• Residual signature: shows which faults are
detectable and isolable from each other
Bipartite-graph based model
Efficient graph-based
algorithms
Sensor placement guideline
Preliminary Results
• Developed methodology & graph-based algorithm
• Applied to simple topologies (2-4 generators)
• Determined minimum number of sensors necessary for full fault isolability
Future Work
• Application to real-size power system topologies
• Dynamic simulation
2008 ESRDC Team Meeting, May, Austin, TX

16. 2008 ESRDC Team Meeting, 20-21 May, Austin, TX
High-End Tools
• CAPS Models, Methodologies & Algorithms
• High-Performance Real Time Digital Simulator
• Virtual Test Bed
• Hardware-in-the-Loop
• PC-Based Software
MATLAB/Simulink, PSCAD, PSIM etc.
2008 ESRDC Team Meeting, May, Austin, TX

17. 5 MW RTDS-PHIL Facility at CAPS
5 MW AC-DC-AC
PEBB-based Converter “Amplifier”
Reproduces simulated voltage waveforms
4.16 kVAC, 1.15 kVDC nominal
+20%/-100% (400) Hz
Bandwidth up to 1.2 kHz
Equipment delivered 10/01/2007 Commissioning started 10/22/2007

18. Design drawing by ABB 06/09/2005
5 MW VVS – 3-Line Diagram
Grid connection
4.16 kV
Design drawing by ABB 06/09/2005
DC load connection
1.15 kV
AC load connection 4.16 kV

19. PHIL Experiments with a Superconducting Fault Current Limiter (FCL)
First user application of 5 MW VVS
Medium voltage FCLs may be applied to ship systems
FCL is a non-linear device posing some challenges to PHIL
Peak power was 1.4 MW
Current tracking within 10% of reference
Measured FCL voltage and current
Prospective current
FCL voltage
Limited current
1.8 kV FCL
Cryostat

20. Future: High Speed Machinery HIL Facility
Machine and system simulations in RTDS
Secured funding to establish experimental facilities for medium (3,600 RMP) and high-speed (22,500 RPM) rotating machinery
Allows for testing high speed generators, motors, or gas turbines
Hz 0…4.16 kV 5 MW / 6.25MVA
2 –stage gear box proposed under DURIP
Recommended for funding by ONR

21. Future HIL R&D at CAPS
Improving HIL interface algorythms for Non-linear loads
Accomodate large changes of apparent impedances
Provide robustness against noise in fedback signals and unpredicted load behavior
Improve transient response of 5 MW VVS
Devolping „virtual“ motor capability using RTDS and various amplifier converters
Will alow testing of motor drives w/o the need to install a real load machine
Implementing of high-rpm machinery test capability
Applies a recently pateted method for HiFi torque control on load side of gear box
Characterizing of CAPS test bed for HiFi modeling
Facilitates future user projects through transparent model sharing
Geographically distributed simulations
Collaboration with MSU (RTDS) and University of Alberta, Canada (OPAL-RT)