۱- Introduction

۲- Proposed methodology

۳- Probabilistic dynamic programming algorithm

۴- Numerical example

۵- Conclusion

References

Abstract

This paper presents a probabilistic dynamic programming algorithm to obtain the optimal cost-efective maintenance policy for a power cable. The algorithm determines the states which a cable might visit in the future and solves the functional equations of probabilistic dynamic programming by backward induction process. The optimisation model considers the probabilistic nature of cables failures. This work specifes the data needs, and presents a procedure to utilize maintenance data, failure data, cost data, and condition monitoring or diagnostic test data. The model can be used by power utility managers and regulators to assess the fnancial risk and schedule maintenance.

Introduction

Power cables play an integral part in the transmission and distribution of electricity. The reliability of power cable contributes substantially towards the reliability of the entire electrical distribution network. The unexpected outages due to the failure of the power cables have a severe impact on utility companies due to tight economic requisites and regulatory pressure. This has engendered a demand for high reliability and a need for the extension of cable life with minimum maintenance cost which can only be achieved by implementation of an efective maintenance policy. In recent years, many methods have been proposed and utilized for the maintenance and replacement of engineering assets; among them, dynamic programming is the most widely used. The dynamic programming approach can provide the optimal cost-efective and reliability-centered maintenance policy for the assets which are required to operate indefnitely. Moghaddam and Usher (2011) presented two dynamic programming-based models to determine the optimal maintenance schedule for a repairable component which has an increasing failure rate. The objective of the two models was to obtain maintenance decision, such that it minimizes total cost subjected to a constraint on reliability and maximizes reliability subjected to a budget constraint on overall cost. In another paper, Korpijärvi and Kortelainen (2009) showed the application of dynamic programming for the maintenance of electric distribution system. Abbasi et al. (2009) developed a priority-based dynamic programming model to schedule the maintenance of the overhead distributed network. They adopted a risk management approach to consider the actual condition of the electrical components and expected fnancial risk in the model. An application of dynamic programming for maintenance of power cable was presented by Bloom et al. (2006). The model represents life-cycle cost approach and it can provide an appropriate time to utilize diagnostic test information in a cost-efective manner. However, the model fails to consider the random failure behaviour of the cable and does not optimize the cost of diferent maintenance decisions.