Risk Based Maintenance. Breakers using

Transcription

1 Risk Based Maintenance Scheduling of Circuit Breakers using Condition-Based d Data Satish Natti Graduate Student, TAMU Advisor: Dr. Mladen Kezunovic

2 Outline Introduction CB Monitoring Maintenance Quantification Model Risk Based Maintenance Approach Case Studies Summary of Achievements

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

4 Introduction: Comparison of Existing and Proposed Researches Operation decision RCM, AMP, Riskbased, RCAM Maintenance Strategies Risk-based decision approach Failure rate, Probabilistic maintenance Models Quantification of maintenance Probabilistic approach via performance indices Condition-based Data

5 Introduction: Expected Contribution Probability Between Limits is Bus 14 L23 Bus 19 L29 BB1 B1 B2 Bus 15 L24 B4 B5 B7 B3 B6 B8 G Density BB2 L Lower Critical Value Upper (msec) Load Bus s17 Condition Based Data CB Control Circuit Signal Processing Performance Indices CB Failure Bayesian Approach Risk Analysis Probability Consequence Risk System Maintenance Risk Reduction Optimization

6 CB Monitoring Over view of monitoring choices: Operating Mechanism - Contact ttravel ltime Measurement - Control Circuit Monitoring - Vibration Analysis Contacts - Resistance Test - Temperature Monitoring Inspection of oil (oil circuit breakers) Partial Discharge

7 CB Monitoring: Data from CBMs Close Initiate Trip Initiate 52X/a 52Y/b CC 52Y/b 52X 52a 52Y 52a TC 52Y/a Control DC + Control DC _ CBM Portable Devices

8 CB Monitoring: Data from CBMs EVENT Waveform abnormalities and signal parameters EVENT DECRIPTION SIGNAL 1 Trip or close operation is initiated (Trip or close initiate signal changes from LOW to HIGH) 2 Coil current picks up 3 Coil current dips after saturation 4 Coil current drops off 5 B contact breaks or makes (a change of status from LOW to HIGH or vice versa) 6 A contact breaks or makes 7 Phase currents breaks or makes 8 X coil current picks up 9 X coil current drops off 10 Y coil current picks up T1 T2 T3 T4 T5 T6 T7 T8 T9 T10

9 CB Monitoring: Data from CBMs Summary of Test Records During Closing Operation of Circuit Breaker Manufacturer and Type: GE VIB Date T2 (sec) T3(sec) T4(sec) T5(sec) T6(sec) 2/12/ /12/ /13/ /13/ /19/ /21/ /11/ /11/ /11/ /11/ /11/

10 Maintenance Quantification Model 0.35 Probability Between Limits is Density History of control circuit signals Lower Upper (msec) Critical Value Extract signal parameters (T1-T10) and fit distribution i i to each parameter Define performance indices using parameter distributions Monitored control circuit data Bayesian approach to update parameter distribution t 1 (msec) t 2 (msec) t 3 (msec) t 4 (msec) t 5 (msec)

11 Assessment of CB Condition P(t i ) is defined d as the probability bilit that t the parameter t i fll falls in the predefined interval, and is given by p( ti ) Pr( li ti ui ) As long as the parameter t i falls in the specified interval, it is said that there is no violation with t i Probability Between Limits is Density p i Lower Upper (msec) Critical Value

12 Performance Indices Performance of close/trip coil Coil Free Travel Time Performance of Auxiliary contacts Contacts Mechanism Travel Time p ( CC) 1 p ( t ) p ( t ) p ( t ) ( AB) 1 p ( t ) p ( t ) p f ( 2 3 t4 p f ( FT ) 1 p( t2 ) p( t3) p f ( 5 t6 p f ( MT) 1 p( t3) p( t5 ) Performance of breaker Failure Probability Index p f 6 ( Br) 1 p( t i ) i 2

13 Bayesian Updating Approach Initial Data Analysis Scatter plot analysis Interdependency among parameters Formulation Normal distribution ib ti Prior Likelihood Posterior Implementation MCMC Updated distributions

14 Sequential Bayesian Approach Data Likelihood Prior Posterior y 1 π 0 L(Y) P(θ Y) y n Bayesian Data Likelihood Prior Posterior y 1 L(y 1 ) π 0 P (θ y 1 ) y 2 L(y 2 ) P (θ y 2 ) Sequential Bayesian y n L(y n ) P (θ y n )

15 Concept of Risk Event, E Failure of a component or group of components Line, Bus bar, Breaker Event Probability, p(e) Control circuit data Failure probability index Event Consequence, con(e) Loss of Load (CCDF) Loss of Line (OPF) Loss of Generator (OPF) Risk(E) p(e)*con(e) Risk associated with each event Risk reduction Maintenance decisions

16 Optimized problem formulation Max N Ri x i 1 i ST : N i 0 c i x i C x 0 or 1 i Where, i : index on breaker N : Total number of breakers c : Maintenance cost of breaker 'i' i RR i : Risk reduction by maintaning it i breaker 'i' C : Total budget This optimization problem is a standard Knap-sack problem This optimization problem is a standard Knap sack problem and can be solved using dynamic programming techniques

17 Case Studies List of case studies Category Case study # Details of the data Maintenance Quantification Model Risk based maintenance Optimization i Case study I Case study II Case study III Case study IV CB control circuit data during OPEN operation CB control circuit data during CLOSE operation Approximation to the Bayesian approach in case studies I & II Bus 16 of IEEE Reliability Test System

18 Case Study I: Open Operation The sequence of occurrence of timing i of parameters during opening is: t 2 -t 3 -t 6 -t 4 -t 5. Rename them as y 1 -y 5 in that order y 1, y 2 and y 3 can be treated as independent. y 4 =β 0 +β 1 y 3 +ε 4 y 5 = β 0 + β 1 y 3 + β 2 y 4 + ε 5 Tolerance Limits for Open Operation Event Lower Upper (msec) (msec) t Scatter plot analysis of timing parameters t t t t

19 Case Study I: Open Operation Summary of Analysis for Open Operation Performance Index Observations Maintenance required? pf(tc) Abnormal behavior of Yes trip coil current. pf(ab) Auxiliary contacts are No operating properly pf(ft) Abnormal free travel Yes times. Improper pope operation of trip latch mechanism pf(mt) Abnormal mechanism Yes travel times. Improper operation of operating mechanism. pf(br) Improper operation of Yes breaker as a whole Performance indices for CB opening

20 Case Study II: Close Operation The sequence of occurrence of timing of parameters during opening is: t 2 -t 3 -t 4 -t 5 -t 6. Rename them as y 1 -y 5 in that order y 1, y 2, y 3 and y 4 can be treated t as independent. d y 5 =β 0 +β 1 y 4 +ε 5. Tolerance Limits for Close Operation Scatter plot analysis of timing parameters Event Lower (msec) Upper (msec) t t t t t

21 Case Study II: Close Operation Summary of Analysis for Close Operation Performance Index Observations Maintenance required? pf(cc) Abnormal behavior of Yes close coil current. pf(ab) Auxiliary contacts are No operating properly. pf(ft) Abnormal free travel Yes times. Improper operation of close latch mechanism. pf(mt) Abnormal mechanism Yes travel times. Improper operation of operating mechanism. pf(br) Improper operation of Yes breaker as a whole. Performance indices for CB closing

22 Case Study III: Comparison CB opening Comparison of index p f (Br) between Bayesian and Sequential Bayesian approaches CB closing

23 Case Study IV: Risk Based System Maintenance Bus 14 L23 Bus 19 L29 BB1 BB2 B1 B2 Bus 15 L24 B4 B5 B7 B3 B6 B8 Load Bus 17 Substation configuration of bus 16 L28 G 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?

24 Case Study IV: List of Events Event Event Event Definition Definition # # # Definition E1 Fault on BB1 E15 Fault on L28 E29 Fault on B2, B3 fails E2 Fault on BB1, B1 fails E16 Fault on L28, B5 fails E30 Fault on B3 E3 Fault on BB1, B4 fails E17 Fault on L28, B6 fails E31 Fault on B3, B6 fails E4 Fault on BB1, B7 fails E18 Fault on L29 E32 Fault on B3, B8 fails E5 Fault on BB2 E19 Fault on L29, B2 fails E33 Fault on B4 E6 Fault on BB2, B3 fails E20 Fault on L29, B3 fails E34 Fault on B4, B5 fails E7 Fault on BB2, B6 fails E21 Fault on G E35 Fault on B4, B7 fails E8 Fault on BB2, B8 fails E22 Fault on G, B7 fails E36 Fault on B5 E9 Fault on L23 E23 Fault on G, B8 fails E37 Fault on B5, B6 fails E10 Fault on L23, B1 fails E24 Fault on B1 E38 Fault on B6 E11 Fault on L23, B2 fails E25 Fault on B1, B2 fails E39 Fault on B6, B8 fails E12 Fault on L24 E26 Fault on B1, B4 fails E40 Fault on B7 E13 Fault on L24, B4 fails E27 Fault on B1, B7 fails E41 Fault on B7, B8 fails E14 Fault on L24, B5 fails E28 Fault on B2 E42 Fault on B8

25 Case Study IV: Event Risk Risk curves Risk associated with each event and breaker

26 Case Study IV: Risk Reduction Risk( E) p( E) Con( E) Interesting to note that, the amount of risk reduced dby maintaining B6 is less compared to B3 and B B3 and B8 should be given 4000 priority based on the risk 2000 reduction levels 0 Risk Reduction Event For the test system under consideration, it can be concluded that, t breakers B3 and B8 are more important t 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

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 thonavar Texas A&M University: Mladen Kezunovic Chanan Singh

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, 10 th Int. Conference on Probabilistic Methods Applied to Power Systems, Rincon, Puerto Rico, May M. Kezunovic, E. Akleman, M. Knezev, O. Gonan and S. Natti, Optimized Fault Location, IREP Symposium 2007, Charleston, South Carolina, August 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 M. Kezunovic and S. Natti, Risk-Based Maintenance Approach: A Case of Circuit Breaker Condition i Based Monitoring, i 3 rd International CIGREWorkshop on Liberalization and Modernization of Power Systems, Irkutsk, Russia, August M. Kezunovic and S. Natti, Condition Monitoring and Diagnostics Using Operational and Non-operational Data, CMD 2006, Pusan, Korea, March S. Natti, M. Kezunovic and C. Singh, Sensitivity Analysis on Probabilistic Maintenance Model of Circuit Breaker, 9 th Int. Conference on Probabilistic Methods Applied to Power Systems, Stockholm, Sweden, June 11-15, S. Natti, P. Jirutitijaroen, M. Kezunovic and C. Singh, Circuit Breaker and Transformer Inspection and Maintenance: Probabilistic Models, 8 th Int. Conference on Probabilistic Methods Applied to Power Systems, Ames, Iowa, September 2004.