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2008 ESRDC Team Meeting, 20 -21 May, Austin, TX Computational Tools for Early Stage 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

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

Challenges & Goals Challenges - Increased complexity and integration of all-electric ship subsystems Uncertainty 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 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

CAPS Development • Real-Time Particle Swarm Optimization for PMSM Parameter Identification • Neural Network 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 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

Real-Time Particle Swarm Optimization for PMSM Parameter Identification Achievements & Considerations • Developed a Real-Time Particle Swarm Optimization for PMSM Parameter Identification Achievements & Considerations • Developed a method to conduct fasterthan-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, 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 • 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 Achievements • Introduced a novel NN-based adaptive nonlinear controller design • Tested the control algorithm using Simulink and PSIM • Tested the algorithm using d. SPACE, RTDS-based Hardware-In-The-Loop Structure of the adaptive NN controller Future Research • 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 / 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 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. 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 Power Loss Control, Propulsion, Combat, Service Loads Mission failure 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 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 Susceptibility Larger-system algorithms Dynamic simulation for full system Fault detection & isolation 2008 ESRDC Team Meeting, 20 -21 May, Austin, TX Web

Structural Analysis for Automated Fault Detection & Isolation D. Düştegör, S. V. Poroseva, S. 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: 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

High-End Tools • CAPS Models, Methodologies & Algorithms • High-Performance Real Time Digital Simulator 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 5 MW RTDS-PHIL Facility at CAPS 5 MW AC-DC-AC PEBB-based Converter “Amplifier” Reproduces simulated voltage waveforms 4. 16 k. VAC, 1. 15 k. VDC nominal +20%/-100% 40 -65 (400) Hz Bandwidth up to 1. 2 k. Hz Equipment delivered 10/01/2007 Commissioning started 10/22/2007

5 MW VVS – 3 -Line Diagram Grid connection 4. 16 k. V DC 5 MW VVS – 3 -Line Diagram Grid connection 4. 16 k. V DC load connection 1. 15 k. V Design drawing by ABB AC load connection 4. 16 k. V 06/09/2005

PHIL Experiments with a Superconducting Fault Current Limiter (FCL) • First user application of 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 nonlinear device posing some challenges to PHIL • Peak power was 1. 4 MW • Current tracking within 10% of reference Prospective current FCL voltage Limited current 1. 8 k. V FCL Measured FCL voltage and current Cryostat

Future: High Speed Machinery HIL Facility Machine and system simulations in RTDS • Secured 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 k. V 5 MW / 6. 25 MVA 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 – 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 Hi. Fi torque control on load side of gear box • Characterizing of CAPS test bed for Hi. Fi modeling – Facilitates future user projects through transparent model sharing • Geographically distributed simulations – Collaboration with MSU (RTDS) and University of Alberta, Canada (OPAL-RT)