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Risk Based Maintenance Scheduling of Circuit Breakers using Condition-Based Data

PS. ERC. Risk Based Maintenance Scheduling of Circuit Breakers using Condition-Based Data. Satish Natti Graduate Student, TAMU Advisor: Dr. Mladen Kezunovic. Outline. Introduction CB Monitoring Maintenance Quantification Model Risk Based Maintenance Approach Case Studies

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Risk Based Maintenance Scheduling of Circuit Breakers using Condition-Based Data

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  1. PS ERC Risk Based Maintenance Scheduling of Circuit Breakers using Condition-Based Data Satish Natti Graduate Student, TAMU Advisor: Dr. Mladen Kezunovic IAB Meeting, Dec. 4-5, 2008

  2. Outline • Introduction • CB Monitoring • Maintenance Quantification Model • Risk Based Maintenance Approach • Case Studies • Summary of Achievements IAB Meeting, Dec. 4-5, 2008

  3. Introduction:Problem Formulation • If it is the same availability of the labor crew, and the labor hours, and the given budget is constrained, how the maintenance decisions need to be implemented (revised)? • Develop: - Maintenance quantification model - component level maintenance strategy - system level maintenance strategy • Apply the developments to: - individual circuit breakers - Multiple circuit breakers in a power system simultaneously IAB Meeting, Dec. 4-5, 2008

  4. Operationdecision Risk-based decision approach RCM, AMP, Risk-based, RCAM Maintenance Strategies Probabilistic approach via performance indices Failure rate, Probabilistic maintenance Models Quantification of maintenance Condition-based Data Introduction:Comparison of Existing and Proposed Researches IAB Meeting, Dec. 4-5, 2008

  5. Bus 15 L24 BB1 G B1 B4 B7 Bus 14 L23 B2 B5 L29 Bus 19 B3 B6 B8 BB2 L28 Bus 17 Load Introduction: Expected Contribution

  6. CB Monitoring Over view of monitoring choices: • Operating Mechanism -Contact Travel time Measurement - Control Circuit Monitoring - Vibration Analysis • Contacts -Resistance Test - Temperature Monitoring • Inspection of oil (oil circuit breakers) • Partial Discharge IAB Meeting, Dec. 4-5, 2008

  7. CB Monitoring: Data from CBMs Close Trip Initiate Initiate Control DC + 52a 52X/a 52 Y 52Y/b 52Y/b TC CC 52 52Y/a X 52a Control DC _ CBM Portable Devices IAB Meeting, Dec. 4-5, 2008

  8. CB Monitoring: Data from CBMs Waveform abnormalities and signal parameters IAB Meeting, Dec. 4-5, 2008

  9. CB Monitoring: Data from CBMs Summary of Test Records During Closing Operation of Circuit Breaker Manufacturer and Type: GE VIB-15.5-20000-2 Date T2 (sec) T3(sec) T4(sec) T5(sec) T6(sec) 2/12/2002 0.001215 0.010417 0.028993 0.056597 0.066840 2/12/2002 0.000868 0.012500 0.032639 0.058160 0.068229 2/13/2002 0.001042 0.014236 0.048785 0.055903 0.066493 2/13/2002 0.001736 0.011979 0.043229 0.052951 0.066146 2/19/2002 0.001389 0.017361 0.037500 0.059896 0.007813 2/21/2002 0.003819 0.004861 0.034375 0.056424 0.067535 6/11/2002 0.001736 0.011285 0.032292 0.063542 0.072917 6/11/2002 0.000868 0.014236 0.031076 0.063021 0.072569 6/11/2002 0.000694 0.010243 0.032465 0.060590 0.070833 6/11/2002 0.000694 0.013889 0.032639 0.061458 0.070486 6/11/2002 0.001042 0.011111 0.048958 0.057118 0.068056

  10. Maintenance Quantification Model History of control circuit signals Define performance indices using parameter distributions Extract signal parameters (T1-T10) and fit distribution to each parameter Monitored control circuit data Bayesian approach to update parameter distribution

  11. Assessment of CB Condition P(ti) is defined as the probability that the parameter ti falls in the predefined interval, and is given by As long as the parameter ‘ti’ falls in the specified interval, it is said that there is no violation with ‘ti’. pi

  12. Performance Indices

  13. Bayesian Updating Approach IAB Meeting, Dec. 4-5, 2008

  14. Data Likelihood Prior Posterior Likelihood Prior Posterior Data y1 π0 π0 L(y1) y1 P (θ| y1) P(θ|Y) L(Y) yn L(y2) y2 P (θ| y2) P (θ| yn) L(yn) yn Sequential Bayesian Approach Bayesian Sequential Bayesian IAB Meeting, Dec. 4-5, 2008

  15. Concept of Risk IAB Meeting, Dec. 4-5, 2008

  16. Optimized problem formulation Where, This optimization problem is a standard Knap-sack problem and can be solved using dynamic programming techniques

  17. Case Studies List of case studies IAB Meeting, Dec. 4-5, 2008

  18. Case Study I: Open Operation • The sequence of occurrence of timing of parameters during opening is: t2-t3-t6-t4-t5. Rename them as y1-y5 in that order • y1, y2 and y3 can be treated as independent. • y4=β0+β1y3+ε4 • y5 = β0 + β1y3 + β2y4+ ε5 Tolerance Limits for Open Operation Scatter plot analysis of timing parameters IAB Meeting, Dec. 4-5, 2008

  19. Case Study I: Open Operation Summary of Analysis for Open Operation Performance indices for CB opening IAB Meeting, Dec. 4-5, 2008

  20. Case Study II: Close Operation • The sequence of occurrence of timing of parameters during opening is: t2-t3-t4-t5-t6. Rename them as y1-y5 in that order • y1, y2, y3 and y4 can be treated as independent. • y5=β0+β1y4+ε5. Tolerance Limits for Close Operation Scatter plot analysis of timing parameters IAB Meeting, Dec. 4-5, 2008

  21. Case Study II: Close Operation Summary of Analysis for Close Operation Performance indices for CB closing IAB Meeting, Dec. 4-5, 2008

  22. Case Study III: Comparison CBopening Comparison of index pf(Br) between Bayesian and Sequential Bayesian approaches CB closing

  23. Case Study IV: Risk Based System Maintenance IEEE 24 bus RTS is considered Generator = 155MW and Load = 100MW 8 breakers (B1-B8) Which breaker needs immediate attention? How to spend a fixed pool of money towards the maintenance of these breakers? Bus 15 L24 BB1 G B1 B4 B7 Bus 14 L23 B2 B5 L29 Bus 19 B3 B6 B8 BB2 L28 Bus 17 Load Substation configuration of bus 16 IAB Meeting, Dec. 4-5, 2008

  24. Case Study IV: List of Events IAB Meeting, Dec. 4-5, 2008

  25. Case Study IV: Event Risk Risk associated with each event and breaker Risk curves IAB Meeting, Dec. 4-5, 2008

  26. Case Study IV: Risk Reduction Interesting to note that, the amount of risk reduced by maintaining B6 is less compared to B3 and B8 B3 and B8 should be given priority based on the risk reduction levels For the test system under consideration, it can be concluded that, breakers B3 and B8 are more important followed by B6 and should be given priority in budget allocation

  27. Summary of Achievements • A probabilistic methodology, ‘Maintenance Quantification Model’ is proposed and implemented • An approximation to the Bayesian approach, called Sequential Bayesian approach is implemented • Risk based system level maintenance strategy is proposed and implemented IAB Meeting, Dec. 4-5, 2008

  28. Financial Support Power Systems Engineering Research Center (Pserc), Project: “Automated Integration of Condition Monitoring with an Optimized Maintenance Scheduler for Circuit Breakers and Power Transformers”. Iowa State University: James D. McCalley Vasant Honavar Texas A&M University: Mladen Kezunovic Chanan Singh IAB Meeting, Dec. 4-5, 2008

  29. Publications • S. Natti and M. Kezunovic, “Assessing Circuit Breaker Performance Using Condition-Based Data and Bayesian Approach”, IEEE Trans. On Power Systems. (In Review). • S. Natti and M. Kezunovic, “Risk-Based Decision Approach for Maintenance Scheduling Strategies for Transmission System Equipment Maintenance”, 10th Int. Conference on Probabilistic Methods Applied to Power Systems, Rincon, Puerto Rico, May 2008. • M. Kezunovic, E. Akleman, M. Knezev, O. Gonan and S. Natti, “Optimized Fault Location”, IREP Symposium 2007, Charleston, South Carolina, August 2007. • S. Natti and M. Kezunovic, “Model for Quantifying the Effect of Circuit Breaker Maintenance Using Condition-Based Data”, Power Tech 2007, Lausanne, Switzerland, July 2007.

  30. S. Natti and M. Kezunovic, “Transmission System Equipment Maintenance: On-line Use of Circuit Breaker Condition Data”, IEEE PES General Meeting, Tampa, Florida, June 2007.  • M. Kezunovic and S. Natti, “Risk-Based Maintenance Approach: A Case of Circuit Breaker Condition Based Monitoring”, 3rd International CIGRE Workshop on Liberalization and Modernization of Power Systems, Irkutsk, Russia, August 2006.  • M. Kezunovic and S. Natti, “Condition Monitoring and Diagnostics Using Operational and Non-operational Data”, CMD 2006, Pusan, Korea, March 2006. • S. Natti, M. Kezunovic and C. Singh, “Sensitivity Analysis on Probabilistic Maintenance Model of Circuit Breaker”, 9th Int. Conference on Probabilistic Methods Applied to Power Systems, Stockholm, Sweden, June 11-15, 2006. • S. Natti, P. Jirutitijaroen, M. Kezunovic and C. Singh, “Circuit Breaker and Transformer Inspection and Maintenance: Probabilistic Models”, 8th Int. Conference on Probabilistic Methods Applied to Power Systems, Ames, Iowa, September 2004.

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