Long-Term Preventive Failure Mitigation Strategy For Transformers Based on Markov Method

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Jianzheng Wang Guoqiang Hu Sungin Cho

Abstract

In this paper, we propose a preventive failure mitigation strategy in the power system based on Markov method. Specifically, we consider multiple units in the system, which are of different types and are managed by a single utility company. To characterize the operation, failure mitigation, and deterioration processes of the equipment, a continuous-time Markov model is formulated. By modelling the failure rate of equipment and the reinstallation rate after failures, the steady state of the proposed Markov model is analytically derived. Then to optimize the long-term net revenue of the utility company, the optimal failure mitigation rate is determined by considering the failure mitigation capacity for each equipment type as well as the overall failure mitigation capacity of the company. The performance of the proposed algorithms is demonstrated with three types of transformers in the simulation.  

Abstract 129 | PDF Downloads 153

##plugins.themes.bootstrap3.article.details##

Keywords

Failure mitigation, Markov method, Power system

References
Ge, H. (2010). Maintenance optimization for substations with aging equipment (2010). Electrical Engineering Theses and Dissertations. Paper, 7.

Ge, H., & Asgarpoor, S. (2012). Reliability and maintainability improvement of substations with aging infrastructure. IEEE Transactions on Power Delivery, 27(4), 1868–1876.

Jiang, R. (2009). An accurate approximate solution of optimal sequential age replacement policy for a finitetime horizon. Reliability Engineering & System Safety, 94(8), 1245–1250.

Rasmekomen, N., & Parlikad, A. K. (2013). Maintenance optimization for asset systems with dependent performance degradation. IEEE Transactions on Reliability, 62(2), 362–367.

Srinivasan, R., & Parlikad, A. K. (2014). Semi-markov decision process with partial information for maintenance decisions. IEEE Transactions on Reliability, 63(4), 891–898.

Taghipour, S., & Azimpoor, S. (2018). Joint optimization of jobs sequence and inspection policy for a single system with two-stage failure process. IEEE Transactions on Reliability, 67(1), 156–169.

Tian, Z., & Liao, H. (2011). Condition based maintenance optimization for multi-component systems using proportional hazards model. Reliability Engineering & System Safety, 96(5), 581–589.

Van Oosterom, C., Peng, H., & van Houtum, G.-J. (2017). Maintenance optimization for a markovian deteriorating system with population heterogeneity. Iise Transactions, 49(1), 96–109.

Wang, R. (2016). A maintenance strategy of improving the reliability of relay protection based on markov model. In 2016 5th international conference on environment, materials, chemistry and power electronics (pp. 124– 130).

Wang, Y., & Pham, H. (2011). A multi-objective optimization of imperfect preventive maintenance policy for dependent competing risk systems with hidden failure. IEEE Transactions on Reliability, 60(4), 770–781.

Wu, F., Niknam, S. A., & Kobza, J. E. (2015). A cost effective degradation-based maintenance strategy under imperfect repair. Reliability Engineering & System Safety, 144, 234–243.

Xiang, Y., Cassady, C. R., & Pohl, E. A. (2012). Optimal maintenance policies for systems subject to a markovian operating environment. Computers & Industrial Engineering, 62(1), 190–197.

Yssaad, B., Khiat, M., & Chaker, A. (2014). Reliability centered maintenance optimization for power distribution systems. International Journal of Electrical Power & Energy Systems, 55, 108–115.

Zhang, N., Fouladirad, M., & Barros, A. (2018). Optimal imperfect maintenance cost analysis of a two-component system with failure interactions. Reliability Engineering & System Safety, 177, 24–34.

Zhong, C., & Jin, H. (2014). A novel optimal preventive maintenance policy for a cold standby system based on semi-markov theory. European Journal of Operational Research, 232(2), 405–411.
Section
Regular Session Papers