A Model-Based Hybrid Approach for Circuit Breaker Prognostics Encompassing Dynamic Reliability and Uncertainty

Prognostics predictions estimate the remaining useful life (RUL) of assets. This information enables the implementation of condition-based maintenance strategies by scheduling intervention when failure is imminent. Circuit breakers (CBs) are key assets for the correct operation of the power network, fulfilling both a protection and a network reconfiguration role. Certain breakers will perform switching on a deterministic schedule, while operating stochastically in response to network faults. Both types of operation increase wear on the main contact, with high fault currents leading to more rapid aging. This paper presents a hybrid approach for prognostics of CBs, which integrates deterministic and stochastic operation through piecewise deterministic Markov processes. The main contributions of this paper are: 1) the integration of hybrid prognostics models with dynamic reliability concepts for a more accurate RUL forecasting and 2) the uncertain failure threshold modeling to integrate and propagate uncertain failure evaluation levels in the prognostics estimation process. Results show the effect of dynamic operation conditions on prognostics predictions and confirm the potential for its use within a condition-based maintenance strategy.

[1]  R. H. Kaufmann,et al.  The Magic of I2t , 1966 .

[2]  Mitra Fouladirad,et al.  A methodology for probabilistic model-based prognosis , 2013, Eur. J. Oper. Res..

[3]  Jianhui Wang,et al.  Smart Transmission Grid: Vision and Framework , 2010, IEEE Transactions on Smart Grid.

[4]  Andrea Bobbio,et al.  Solving Dynamic Reliability Problems by means of Ordinary and Fluid Stochastic Petri Nets , 2005 .

[5]  M. Kezunovic,et al.  Automated monitoring and analysis of circuit breaker operation , 2005, IEEE Transactions on Power Delivery.

[6]  Matti Lehtonen,et al.  Data Mining of Online Diagnosed Waveforms for Probabilistic Condition Assessment of SF$_{6}$ Circuit Breakers , 2015, IEEE Transactions on Power Delivery.

[7]  S. E. Rudd,et al.  Circuit breaker prognostics using SF6 data , 2011, 2011 IEEE Power and Energy Society General Meeting.

[8]  Ferdinando Chiacchio,et al.  SHyFTA, a Stochastic Hybrid Fault Tree Automaton for the modelling and simulation of dynamic reliability problems , 2016, Expert Syst. Appl..

[9]  Elodie Chanthery,et al.  Hybrid Particle Petri Nets for Systems Health Monitoring under Uncertainty , 2015 .

[10]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[11]  Jose Ignacio Aizpurua,et al.  A cost-benefit approach for the evaluation of prognostics-updated maintenance strategies in complex dynamic systems , 2016 .

[12]  Michael G. Pecht,et al.  An Options Approach for Decision Support of Systems With Prognostic Capabilities , 2012, IEEE Transactions on Reliability.

[13]  Luigi Portinale,et al.  Approaching dynamic reliability with predictive and diagnostic purposes by exploiting dynamic Bayesian networks , 2014 .

[14]  Shankar Sankararaman,et al.  Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction , 2015 .

[15]  Lars Grunske,et al.  Architecture-driven reliability optimization with uncertain model parameters , 2012, J. Syst. Softw..

[16]  M. Bouissou,et al.  From Modelica models to dependability analysis , 2015 .

[17]  Matthew Daigle,et al.  Model-based Prognostics of Hybrid Systems , 2015, Annual Conference of the PHM Society.

[18]  Pierre-Etienne Labeau,et al.  Dynamic reliability: towards an integrated platform for probabilistic risk assessment , 2000, Reliab. Eng. Syst. Saf..

[19]  Jose Ignacio Aizpurua,et al.  Towards a methodology for design of prognostic systems , 2015 .

[20]  Marvin Rausand,et al.  System Reliability Theory: Models, Statistical Methods, and Applications , 2003 .

[21]  Victoria M. Catterson,et al.  An Agent-Based Implementation of Hidden Markov Models for Gas Turbine Condition Monitoring , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  KimJooSeuk,et al.  Robust kernel density estimation , 2012 .

[23]  G. Balzer,et al.  Availability of HV Circuit-Breakers: The Application of Markov Model , 2007, 2007 IEEE Power Engineering Society General Meeting.

[24]  Antoine Grall,et al.  Remaining Useful Lifetime Prognosis of Controlled Systems: A Case of Stochastically Deteriorating Actuator , 2015 .

[25]  Tommie Lindquist,et al.  Circuit breaker failure data and reliability modelling , 2008 .

[26]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[27]  Graeme Burt,et al.  The Power Networks Demonstration Centre: An environment for accelerated testing, demonstration and validation of existing and novel protection and automation systems , 2014 .

[28]  Kai Goebel,et al.  Model-Based Prognostics With Concurrent Damage Progression Processes , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[29]  Ratna Babu Chinnam,et al.  Health-State Estimation and Prognostics in Machining Processes , 2010, IEEE Transactions on Automation Science and Engineering.

[30]  Clayton D. Scott,et al.  Robust kernel density estimation , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[31]  George David Camps The development of a methodology to determine the maintenance strategy for high voltage circuit breakers , 2010 .

[32]  Keith Harker Power System Commissioning and Maintenance Practice , 2011 .

[33]  J.R. McDonald,et al.  Providing Decision Support for the Condition-Based Maintenance of Circuit Breakers Through Data Mining of Trip Coil Current Signatures , 2007, IEEE Transactions on Power Delivery.

[34]  N. Limnios,et al.  A method to compute the transition function of a piecewise deterministic Markov process with application to reliability , 2008 .

[35]  Daniele Codetta-Raiteri Modeling and simulating a benchmark on dynamic reliability as a Stochastic Activity Network , 2011 .

[36]  Jose Ignacio Aizpurua,et al.  On the use of probabilistic model-checking for the verification of prognostics applications , 2015, 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS).

[37]  N. Zanghí,et al.  Probability models , 1984 .

[38]  Sze-jung Wu,et al.  A Neural Network Integrated Decision Support System for Condition-Based Optimal Predictive Maintenance Policy , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[39]  Krishna R. Pattipati,et al.  Model-Based Prognostic Techniques Applied to a Suspension System , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[40]  Luigi Portinale,et al.  Dynamic Bayesian Networks for Fault Detection, Identification, and Recovery in Autonomous Spacecraft , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[41]  Patrik Hilber,et al.  A review of methods for condition monitoring, surveys and statistical analyses of disconnectors and circuit breakers , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[42]  Khac Tuan Huynh,et al.  On the Use of Mean Residual Life as a Condition Index for Condition-Based Maintenance Decision-Making , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[43]  Sheldon M. Ross,et al.  Introduction to probability models , 1975 .

[44]  V. M. Catterson,et al.  Prognostics of transformer paper insulation using statistical particle filtering of on-line data , 2016, IEEE Electrical Insulation Magazine.

[45]  Enrico Zio,et al.  The Monte Carlo Simulation Method for System Reliability and Risk Analysis , 2012 .

[46]  Man Ieee Systems IEEE transactions on systems, man, and cybernetics. Systems , 2013 .

[47]  Jyh-Cherng Gu,et al.  Intelligent maintenance model for condition assessment of circuit breakers using fuzzy set theory and evidential reasoning , 2014 .