Computational Tools for Early Stage Ship Design 2008 ESRDC Team Meeting, 20-21 May, Austin, TX Computational Tools for Early Stage Ship Design Center for Advanced Power Systems Florida State University 2000 Levy Avenue, Tallahassee, FL 32310

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

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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 2 - 4 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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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, 20-21 May, Austin, TX

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% 40-65 (400) Hz Bandwidth up to 1.2 kHz Equipment delivered 10/01/2007 Commissioning started 10/22/2007

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

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

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 40-400 Hz 0…4.16 kV 5 MW / 6.25MVA 2 –stage gear box proposed under DURIP Recommended for funding by ONR

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)