# Combined preventive and predictive maintenance model of power transformers (Case study: Khorasan Regional Electricity Company)

Document Type : Power Article

Authors

1 Islamic Azad University of Neyshabur

2 Hakim Sabzevari University

3 Khorasan Regional Electricity Company (KREC), Mashhad, Iran

4 Islamic Azad University of Neyshabur, Neyshabur, Iran

Abstract

By installing the smart sensor and early detection of minor failures, the predictive maintenance can be added to the periodic maintenance. The purpose of this paper is to present a new preventive and predictive maintenance Markov model. For this at the first step, the various types of equipment failures are analysed. Therefore, a mathematical model is presented to calculate the rates of failures at various levels. In addition, the effect of smart sensor instalation on these failure rates has been modelled. In this modelling, the failure probability of the sensor is also considered.The equipment failures are classified into four levels of failure. The most importants failures rates are the outage failure rate and emergency failure rate. In the second step, a new Integrated preventive and predictive maintenance Markov model to consider the effect of smart sensor installation on equipments in maintenance process is presented. By installing the smart sensor, a number of high severity failures are early identified and corrected by emergency outages. So the frequency of going to major maintenance, the lifetime and maintenance costs in the new Markov model is improved with respect to the preventive maintenance model. At the third step, the failures of 400 kv and 132 kv power transformers of Khorasan Regional Electricity Company (KREC) were studied. The simulation results show that with new Markov model, the outage rates of 400 kv and 132 kv transformers are reduced by 74.12% and 54.51%, respectively, and the lifetimes are increased 28.4 and 20.4, respectively

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Main Subjects

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### History

• Receive Date: 26 August 2018
• Revise Date: 15 November 2018
• Accept Date: 19 December 2018