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A new model for Assessment and Optimization of Number of Spare Transformers and their Locations in Distribution Systems
M Sedaghati, R Dashti,
Volume 11, Issue 4 (12-2015)
Abstract

In this paper, a new model has been presented to determine the number of spare transformers and their locations for distribution stations. The number of spare transformers must be so that they need minimum investment. Furthermore, they must be sufficient for replacing with transformers that have been damaged. For this reason, in this paper a new purpose function has been presented to maximize profit in distribution company’s budgeting and planning. For determining the number of spares that must be available in a stock room, this paper considers the number of spares and transformer’s fault at the same time. The number of spare transformers is determined so that at least one spare transformer will be available for replacing with the failed transformers. This paper considers time required for purchasing or repairing a failed transformer to determine the number of required spare transformers. Furthermore, whatever the number of spare equipment are increased, cost of maintenance will be increased, so an economic comparison must be done between reduced costs from reducing of outage time and increased costs from spare transformers existence.

AWT IMAGE


Optimal Estimation of Weibull Distribution Parameters in order to Provide Preventive-Corrective Maintenance Program for Power Transformers
H. Sh. Solari, B. Majidi, M. Moazzami,
Volume 15, Issue 4 (12-2019)
Abstract
In this paper, a new method for modelling and estimation of reliability parameters of power transformer components in distribution and transmission voltage levels for preventive-corrective maintenance schedule of transformers is proposed. In this method, with optimal estimation of Weibull distribution parameters using least squares method and input data uncertainty reduction, failure rate and probable distributions of power transformers’ components as the key parameters of equipment reliability is estimated. Then by using the results of this modelling, a maintenance schedule for evaluation the effect of maintenance on reliability of this equipment is presented. Simulation results using real failure data of 196 power transformers on 33 to 230kV voltage levels show that applying the proposed method in addition to uncertainty reduction of raw input data and better estimation of equipment reliability, improve decision making regarding maintenance schedule of power transformers.

Integrated Preventive and Predictive Maintenance Markov Model for Circuit Breakers Equipped With Condition Monitoring
A. Karimabadi, M. E. Hajiabadi, E. Kamyab, A. A. Shojaei,
Volume 16, Issue 2 (6-2020)
Abstract
The Circuit Breaker (CB) is one of the most important equipment in power systems. CB must operate reliably to protect power systems as well as to perform tasks such as load disconnection, normal interruption, and fault current interruption. Therefore, the reliable operation of CB can affect the security and stability of power network. In this paper, effects of Condition Monitoring (CM) of CB on the maintenance process and related costs are analyzed. For this, A mathematical formulation to categorize and model equipment failures based on their severity is developed. By CM, some of the high severity failures, named major failures, can be early detected and be corrected as a minor failure. This formulation quantifies the effect of CM on the outage rate and Predictive Maintenance (PDM) rate of equipment. Also, by combining the predictive maintenance to preventive maintenance, the Integrated Preventive and Predictive Maintenance Markov model is presented to analyze the effect of CM on the maintenance process. Finally, the optimal inspection rates of CBs based on the minimum maintenance cost in the traditional and the proposed Markov model are determined. To verify the effectiveness and applicability of the method, the proposed approach is applied to the CBs of KREC in Iran.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.