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Texas A&M University

Texas A&M University. T-17 Enhanced Reliability of Power System Operation Using Advanced Algorithms and IEDs for On-Line Monitoring. IAB Meeting, May 18-20, 2005. Participants. Introduction. Project duration: June 1, 2002-May 31, 2005

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Texas A&M University

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  1. Texas A&M University T-17 Enhanced Reliability of Power System Operation Using Advanced Algorithms and IEDs for On-Line Monitoring IAB Meeting, May 18-20, 2005

  2. Participants

  3. Introduction • Project duration: June 1, 2002-May 31, 2005 • This is an extension of the previous work on Fault location and State estimation • This project was aimed at: - Definition of new concept for automated analysis - Development of algorithms - Demonstration through simulation

  4. Automated Analysis System Remote Control Center PROCESSED DATA DATABASE RAW DATA IEDs FAFL VSDB TSSE SSSV DFRA CBMA DPRA PQMA Background

  5. Objectives • Develop substation automation system which enables: • Integration of data from multiple IEDs • Processing of data at substation level by implementing novel applications • Sharing of results with other substations and remote control centers • Develop methods of detecting and identifying network parameter errors

  6. Substation Developments Software Architecture

  7. Substation Developments Software Architecture

  8. Substation Developments Substation Topology

  9. Substation Developments Integrated GUI

  10. Substation Developments Integrated GUI

  11. Substation Developments IED Data Simulation – Data Flow

  12. Substation Developments IED Data Simulation – Substation Data Model

  13. Substation Developments IED Data Simulation – Faults and Switching Sequences

  14. Substation Developments IED Data Simulation – Errors Insertion

  15. Substation Developments Verification of substation database (VSDB) • Verifies the correctness of substation IED data before they are stored into the substation database. The IEDs can be: • Analog – (currents, voltages) and/or • Digital – (statuses of contacts of circuit breakers)

  16. Substation Developments VSDB - algorithm

  17. Substation Developments • Monitors and verifies switching sequences of circuits breakers in the substation • Traces and concludes what reasons caused extensive switching Substation Switching Sequences Verification (SSSV)

  18. Substation Developments SSSV

  19. Substation Developments • Analyzes the protection (relay and circuit breaker) operations • Verifies data consistency of relay event report and oscillography file Digital Protective Relay Analysis (DPRA)

  20. Substation Developments DPRA Validation and diagnosis of relay operation

  21. Substation Developments DPRA Detecting source of disturbance information

  22. Project Deliverables Software • VSDB analysis application • SSSV analysis application • DPRA analysis application • Integrated GUI • Substation Data Model in ATP enabling simulation of CT, PT, CCVT, CB statuses and DFR data for arbitrary Faults and CB switching sequences • Substation Topology Description in SCL • Digital Protective Relay Model in C++ • Converter from PL4 -> COMTRADE file format • Custom COMTRADE file viewer

  23. Project Deliverables Documentation • Statement of work • Functional Requirements • Implementation Description • Testing • Conclusion • Appendices – demonstration scenarios

  24. Parameter error detection and identification • Causes: Network parameters change due to environmental conditions Network parameters are incorrectly recorded after maintenance Modeling errors may show up as parameter errors

  25. Parameter error detection and identification • Effects: State estimator will generate bad data flags Good measurements will be incorrectly discarded by the bad data processor Estimated state will be BIASED !

  26. Parameter error detection and identification • Challenge: Suspecting errors in all network parameters will lead to an exponentially complex problem. Bad analog measurements may exist simultaneously with incorrect network parameters.

  27. Parameter error detection and identification • Proposed approach: Apply the method of Lagrangian which was previously successfully applied to circuit breaker status error identification. Develop an automated procedure to account for different types of parameter errors (transmission line parameters, transformer taps, shunt cap/reactance).

  28. Parameter Error Identification Method Normalizing λ: Solve for the state variables:

  29. Numerical Example: 14-bus system

  30. Meas. or Constraint Normalized residual / multiplier Test A Test B 256.0877 196.9072 14.1172 109.4084 8.5355 91.3546 165.5449 280.2699 108.4855 172.8344 86.2912 81.0881 Numerical Example: Test A: Measurement p45 is wrong. Test B: Reactance of line 4-5 is wrong.

  31. Benefits: • State vector dimension remains fixed, so even very large scale SE problems can be handled. • Bad analog measurements and parameter errors can be differentiated. • No need to pre-specify the suspect parameter. • Existing WLS state estimator code can be revised to incorporate this capability.

  32. Conclusions • All project goals are accomplished • Deliverables are being prepared (will be available August 31, 2005) • Software demonstration is available (will be presented during the poster session) • Sponsors for future field implementation of the specific scenarios of the proposed concept are found: - EPRI - Department of Energy - Vendor

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