1. MSU and ESRDC Collaborative Efforts 1 Noel N. Schulz and Colleagues

2. 2 MSU – ESRDC Team Dr. Sherif Abdelwahed, Assistant Professor, ECE Dr. Stan Grzybowski, Mississippi Power Endowed Professor, ECE Dr. Herb Ginn, Associate Professor, ECE Dr. Anurag Srivastava, Research Assistant Professor, ECE Dr. Suresh Srivastava, Visiting Professor, IIT-Delhi Dr. Noel Schulz, TVA Endowed Professor, ECE Dr. Stephanie Doane, Professor, Psychology Dr. Tomasz Haupt, Research Professor, Center for Advanced Vehicular Systems

3. Outline MSU and ESRDC Collaborative Efforts –AC-DC Converter for MVDC PHIL Demo (MSU, FSU, USC) –System-Level Converter Control (USC, MSU-Power Electronics, Control, and Human Systems) –Simulation Environment for Dynamic Modeling & Simulation (MSU, UT) –Human-System Engineering –High Voltage and Materials –Power System Activities including MVDC –Protection Research Activities for Shipboard Power Systems Transition Plan 3

4. Controls Human Systems Computational Tools and Visualization MSU ESRDC Activities 4 High Voltage and Materials Power Electronics Power Systems

5. Controls Human Systems Computational Tools and Visualization MSU ESRDC Activities 5 High Voltage and Materials Power Electronics Power Systems

6. 6 Using AMSC PEBBs Utilizing the previously developed multi-functional control for AC/DC converters Represents PCM-4 converter in the MVDC PHIL setup Working with USC and FSU to develop a HIL interface for the MSU controller Red Team (POC): MSU (Ginn) Status: low power testing completed at 480V AC up to 850V DC at 50kVA Next steps: interface MSU controller to HIL The three PCM-4 phase currents during the step in the input-current reference of PCM-1 AC-DC Converter for MVDC PHIL Demo at FSU

7. Systems of Multifunctional Power Electronic Converters A current research focus area at MSU is: Development of control methods that enable the coordinated operation of distributed systems of multi-functional power electronic converters. The current efforts in this direction are: Development of control strategies and algorithms for multiple converters distributed in a system considering the level of centralized versus distributed control. Investigate methods of control reconfiguration of power electronic converters in order to achieve the mission of the interconnected system of converters. This work leverages the multi-functional converter research conducted during the past two years at MSU as well as builds upon the initial work conducted on systems of paralleled converters. Converter and Load Test-bed at MSU. Lab tour available on Wednesday and Thursday!

8. System-level Converter Control in Distributed Electric Networks Challenges Continual balance between supply and demand under variable compensation objectives. Effective and flexible management of energy flow throughout shipboard distribution systems. Distributed implementation to avoid single points of failure, and that is robust, and expandable. Two approaches are under investigation: –Multi-agent Systems (MAS) This work is being performed in collaboration with USC. –Distributed Model-Based Control

9. Distributed Model-Based Approach Objectives Define appropriate multi-level abstractions to represent the dynamics of coordinating multifunctional converters. Develop efficient system-level control policies that can be analyzed for stability and convergence properties. Develop integrated system modeling, analysis, and control synthesis tools. Multi-Agent Approach Objectives (with USC) Determine the agent functions required to coordinate multifunctional converters Assess different functionality partitions among the device controller and agent system level device manager. Develop a method to share a burden among converters with delocalized knowledge of absolute and current loading conditions System-level Converter Control in Distributed Electric Networks

10. Multi-Agent Approach Approach 1: External PEBB Agent (Current) The approach of agents external to the converter has been implemented and some preliminary testing has been conducted Agents communicate with PEBB controllers via Ethernet Case 1 – elaboration at PEBB’s side -The PEBB Agents, one per converter, receive from the converter elaborated data. The Agents receive the terms of the current decomposition that is performed within the PEBB control architecture. The Agents then exchange information and make decisions based on these quantities. Once the decision is made, the Agent implements it by sending the appropriate current reference coefficients to the PEBB Case2 – elaboration at Agent’s side -The control Agents receive voltage and current data from the PEBB and then proceed to further elaboration. The Agents then exchange information and make decisions based on the results of this elaboration. Once the decision is made, the Agent implements it by sending the appropriate current reference coefficients to the PEBB Approach 2: Internal PEBB Agent (Future) The results of the external PEBB agent study will be used to implement agents locally within the PEBB controller Two possibilities for Approach 1 have been investigated:

11. Experimental PEBB Agent Setup Converters 1&2 Load Bank Mini-dc link The test setup incorporates four PEBB converters, two in a back-to-back configuration to simulate energy storage An additional load bank imposes non-linear and unbalanced conditions on the AC system Demo with Lab Tour

12. Controls Human Systems Computational Tools and Visualization MSU ESRDC Activities 12 High Voltage and Materials Power Electronics Power Systems

13. Human-System Interface (HSI) Design, Test, and Evaluate HSI that Optimizes Human-System Performance Optimize energy manager understanding of PEBB agent actions during system monitoring Understanding predicts user ability to remain in-the-loop and intervene when required Initial design in progress, T&E to be completed in coming year

14. Controls Human Systems Computational Tools and Visualization MSU ESRDC Activities 14 High Voltage and Materials Power Electronics Power Systems

15. 15 Abstract Modeling of Distributed Converter System New component-based modeling for distributed converter systems. Modules: –Generator –Load (dynamic, reconfigurable) –Energy storage –Converter (AC/DC, DC/AC) –Converter control structure Links: –Power transmission lined –Measurements –Data/control lines u,i c AC System 1 AC System 2 6-pulse AC/DC Converters configurable load DC bus u,i c c Bi-directional Voltage Source Converter Bi-directional Voltage Source Converter Bi-directional Voltage Source Converter Energy Storage

16. Distributed Model Predictive Control (MPC) MPC: at each step, a finite- horizon optimal control problem is solved but only one step is implemented. In distributed MPC a global controller manages inter- component interaction and enforces global requirements. Abstract representation of the components is used for high- level control decisions. Global control actions are given as additional constraints for local controllers. 16 u,i c AC System 1 AC System 2 6-pulse AC/DC Converters configurable load DC bus u,i c c Bi-directional Voltage Source Converter Bi-directional Voltage Source Converter Bi-directional Voltage Source Converter Energy Storage Local Control structure Local Control structure Local Control structure Global Controller (coordinator) Abstract System Model System measurements Predicted future states Local control commands (constraints) c cc

17. Model-driven Engineering Design data, constraints, operational requirements, and platforms specs Research goal is to provide tools for transforming design specification into formal models that can be used for analysis, simulation, automatic control synthesis, code generation and deployment. Level of Abstraction Domain-specific modeling languages Provide support for system modeling through a composition of predefined component templates Domain-independent modeling languages State Charts Simulink models Provide support for system analysis, testing, and simulation through formal models Control Libraries Measurement support Active state models Hardware Execution Kernel (OS)

18. Controls Human Systems Computational Tools and Visualization MSU ESRDC Activities 18 High Voltage and Materials Power Electronics Power Systems

19. Simulation Environment for Dynamic Thermal Modeling and Simulation (DTMS) Framework T. Haupt, G. Henley, B. Parihar Mississippi State University in collaboration with T. Kiehne and M. Pierce University of Texas at Austin

20. Simulation Environment for DTMS  An input language was designed to serve as an interface between the GUI and DTMS,  A parser for this language was written and included in DTMS,  An input file generator was written for the GUI to create the input file based on the information in the database plus parameters or object state information input by the user,  The runtime environment and GUI for Fire&Smoke has been adapted for DTMS. The DTMS Framework is a modeling and simulation platform designed for system-level thermal simulations of indefinitely large shipboard systems. DTMS supports a customizable input/output system designed to allow it to be easily integrated with external platforms and systems. MSU developed a GUI (Graphical User Interface) and a runtime environment for setting up simulations, running DTMS and visualizing the results.

21. Architecture of the system CAD Processing Thermal Model Graphical User Interface Output (Simulation Results) Input (Simulation Setup) (Third-party Dynamic Thermal Modeling and Simulation Framework). DTMS Properties Once the CAD Processing is complete and the geometry is acceptable, the database contains the necessary information to run simulations. Geometry and properties Database CAD file conditioning CAD file conditioning CAD data extraction dxf

22. DTMS GUI features  3D ship view with rotate, zoom, translate, and deck separation feature for better interior views.  Point and click operation for most simulation setup  Automatic generation of input decks  User-selected display for various objects, compartments, and decks.  User-defined color maps and legends.  Real time visualizations Test case: a simplified A/C plant Poster with Demo and Lab Tour on Thursday

23. Controls Human Systems Computational Tools and Visualization MSU ESRDC Activities 23 High Voltage and Materials Power Electronics Power Systems

24. Human-System Engineering Integrate Human-System Interface (HSI) and Tool Design Process to Facilitate Tool Transition to Fleet –Design power management tools and HSI in tandem –Test and Evaluate in tandem Aided interface Experiments performed that asked ECE students to redistribute power using an unaided or an aided interface. The interfaces were similar except for DSS advice User interactions with the interface were recorded Scoring rules designed to measure reconfiguration quality applied to each user’s aided and unaided reconfigurations

25. 25 Scores for aided and unaided interfaces of matched problems (for 10 users) Matched problem s Mean Aided Mean Unaid ed S.D. Aided S.D. Unaided 185.52.5 2.8 251.45.2 2.2 343.54.5 3.3 46.533.3 2.5 582.24.2 2.4 69.551.5 2.3 Total4120.6 Overall Mean 6.83.4 Poster and Tour/Talk on Thursday

26. Controls Human Systems Computational Tools and Visualization MSU ESRDC Activities 26 High Voltage and Materials Power Electronics Power Systems

27. 27 Accelerated Electrical Degradation of Machine Winding Insulation Dielectric Test System 0 to 3500 V, 40 kHz, 20ºC to 260ºC Pulse waveform Power electronic devices cause voltage spikes and harmonics that reduce the machine winding electrical insulation strength Accelerated degradations of machine winding insulation was obtained by high frequency pulse voltage and high temperature stresses Evaluation of electrical properties was performed for samples before and after 100, 500, and 1000 hours of accelerated degradation at 20 kHz, and 40 kHz square pulse frequency and 90% of rated temperature

28. 28 Accelerated Electrical Degradation of Machine Winding Insulation Partial discharge inception voltage (PDIV) as a function of time Aging conditions: a) 1300 V square pulse at 40 kHz MW 35-C at 180 ºC, and MW 80-C at 140 ºC b) 1350 V square pulse at 20 kHz MW 16-C at 216 ºC, and MW 73-C at 200 ºC Breakdown Voltage as a function of time Aging conditions: a) 1300 V square pulse at 40 kHz MW 35-C at 180 ºC, and MW 80-C at 140 ºC b) 1350 V square pulse at 20 kHz MW 16-C at 216 ºC, and MW 73-C at 200 ºC

29. Measurement of Partial Discharge in Machine Winding Insulation During 50 ns Rise Time Pulse Voltage 1300V Square Pulse <40 ns Rise Time Measurement System Heat Path Heat Injection Samples are Inserted in the Cylindrical Shielding Outer Shield Sample Shielding 1 GHz Oscilloscope Ch.1 Ch.2 Ch.3 Diff. Probe Z Z Z Sample 1 Sample 2 Sample 3 Pulse Source Vm Wire Connectors: System Conn. Generator and Probes Ground Pulse Voltage

30. 30 Measurement of Partial Discharge in Machine Winding Insulation During 50 ns Rise Time Pulse Voltage Partial Discharge Patterns 1300 V Square Pulse, <40 ns Rise Time Partial Discharge at 20  CPartial Discharge at 150  C

31. Electrical Degradation of 15 kV XLPE and EPR Cables Energized by Switching Impulses 250/2500 μs of 100 kV Cross-section of XLPE and EPR cables

32. 32 Electrical Degradation of 15 kV XLPE and EPR Cables Energized for 100, 500, 1000, 5000 and 10000 switching impulses (a) (b) Inception voltage of partial discharge: (a) EPR cable samples (b) XLPE and EPR cable samples Posters and Tours on Wednesday and Thursday

33. Controls Human Systems Computational Tools and Visualization MSU ESRDC Activities 33 High Voltage and Materials Power Electronics Power Systems

34. Objective: To increase the survivability of SPS with increased security and reliability Key Issues: –Analysis of MVDC system - Stability –Reconfiguration - Protection Approach at MSU: –MVDC (Architecture, small signal & transient stability, optimal voltage and power control) –Stability (Tool development for index based and continuation power flow) –Reconfiguration (Survivability index, cognitive analysis and uncertainty) –Protection (Protection scheme, relay model, hardware in the loop with multiple platform) Power System Research Activities Related to SPS Including MVDC System

35. Motivation Conventional tool used for stability analysis of utility can not be extended for SPS due to pulse load, EMALS, AC/DC, 3 phase unbalanced network Need tools for electric ship voltage stability assessment to investigate and study stability margin and comparing different SPS architecture Objective Index based: To develop fast and robust algorithm to find out static and dynamic voltage stability index for all-electric ship to calculate approximate stability margin Continuation power flow: To develop a tool to accurately calculate stability margin for setting up benchmarks for index based approach of voltage stability Index based approach Continuation power flow based approach Stability of Shipboard Power System

36. IEEE 9-Bus Index based approach for Stability of SPS Load factor Minimum Eigenvalue 1.0 5.709206867 1.1 5.627212602 1.2 5.529382811 1.3 5.413750157 1.4 5.277671071 1.5 5.117537598 1.6 4.928309384 1.7 4.702347895 1.8 4.428685008 1.9 4.08863468 2.0 3.648121503 2.1 3.031325541 2.2 1.97980143 2.25 0.639127222 2.3 4.328855049 Approach Eigenvalue based sensitive analysis Second order performance index based on hessian matrix by calculating derivative of Jacobean matrix Dynamic stability index is being developed

37. Approach: –Predictor-Corrector based –Tangent vector based –Pseudo arc length and local parameterization –Adaptive step length Continuation Power Flow for Stability of SPS 21 5 6 7 8 9 10 11 1212 13 14 15 16 17 18 GG 4 GG 3 Healy System

38. Stability Studies of Shipboard Power System for MVDC architecture. Motivation: The adoption of a new architecture (MVDC) for a shipboard power system, requires investigating the stability of the system under various system faults or disturbance conditions. Work carried out: Detailed analysis of small signal stability of the system has been carried out. Participation analysis used to identify the controls required to improve the stability. Currently studying the transient stability. Results: Small signal stability based on Eigenvalue analysis has been carried out on MVAC as well as MVDC architectures of the zonal shipboard power system. Participation analysis reveals that the excitation/load bus voltage states are contributing maximum to the critical modes. Lower order exciter control (type-0 exciter) exhibited poor damping with MVAC and MVDC architectures. Use of Higher order (IEEE type-1) exciter or shunt compensating device like SVC improved the damping of the system.

39. 39 Summary of Small Signal Stability Results Table: Few Eigenvalues close to imaginary axis

40. Protection Research Activities for Shipboard Power System Development of protection scheme Relay modeling using NI, dSpace, RTDS Hardware in the loop with multiple platform Multiple relay operation and coordination Work Completed Developed multiple hardware in the loop platform using dSpace, National Instruments and RTDS Relay modeling and prototyping using Simulink & LabView for overcurrent and differential relay Multiple relay coordination for shipboard power system protection

41. Schweitzer Relays (SEL- 421, SEL- 351) Shipboard Power System (SPS) RTDSVTB-RT Relay Hardware Relay model Shipboard Power System (SPS) RTDSVTB-RT PXIdSPACE Relay Prototype Protection Research Activities Lab tours available Wednesday and Thursday!

42. Major planned tasks: –Development of dynamic voltage stability index –Development of continuation power flow for distribution system based on current injection –Reconfiguration using bacteria foraging optimization –User interface for developed reconfiguration algorithm for SPS –Multiple relay operation using RTDS and SEL and GE relay –Integrating reconfiguration technique with controller in the loop to perform real time reconfiguration –Detailed Converter dynamics to be considered, while performing small signal stability analysis. – Transient stability analysis considering faults at AC and DC bus. –Design of supplementary controllers to Voltage Source Converters(VSCs), if required to improve the system stability. Intelligent reconfiguration and stability index algorithm Restored load with optimized survivability Planned Tasks for 2009-2010

43. Transition Plan for MSU ESRDC Team Herb Ginn will transition to the MSU Representative on the ESRDC Board of Directors Noel Schulz will continue as a researcher on the MSU team for one year from Kansas State. MSU has hired a new faculty member in the power systems area, Yong Fu from IIT-Chicago An additional hire is expected in 2009-2010 43