Abstract
The research is devoted to the development of a model for cost distribution of maintenance and repair of electrical equipment when making decisions on the management of the electric power system state. The decrease in the reliability of electric power system operation, caused by the objectively existing aging of electrical equipment, requires consideration of equipment significance when planning the maintenance and repair. For this purpose, it is proposed to use the theory of fuzzy sets, Saati’s method and Boolean programming method. The result of solving the optimization problem of multicriteria analysis is a vector of the best alternatives, built on the principle of dominance. The developed algorithm of complex simulation of the electric power system state and cost distribution of maintenance and repair for making decisions on the determination of priority of electrical equipment out of service allows for effective decisionmaking. The results of probabilistic and statistical simulation of electric power system states using the Monte Carlo method allow us to take into account the probabilistic nature of emergency situations in the electric power system when determining its weakest elements that require priority replacement. The advantage of the proposed approach is taking into account the technical condition of electrical equipment for risk assessment of the electric power system emergency situation. A comparative analysis of ranking results of electrical equipment based on the emergency risk assessment of the electric power system confirmed the high efficiency of the planning of electric power system states when solving the problems of preventive control. The developed model will be used for further research and development of the algorithm for making effective decisions regarding the operation strategy of the electrical equipment and preventive control of the subsystem operation of the electric power system. The obtained results of complex simulation of the electric power system state and technical condition of the electrical equipment give grounds to assert the possibility of software implementation of operation risk analysis of the electric power system for power supply companies
Highlights
IntroductionAn analysis of the current state and further ways of development of the electric power industry shows that its reformation is carried out in a number of contradictions between economic relations and operation reliability of electric power system [1]
We considered the influence of accidental changes in the electrical network topology associated with the planned or emergency out of service of the electrical equipment or group of electrical equipment on the emergency risk under electrical equipment failure, in particular, the technical risk assessment of disturbance of dynamic stability
The mathematical model of aggregated estimation of the significance degree of groups of electrical equipment in terms of cost distribution for maintenance and repair was developed on the basis of using the theory of fuzzy sets, Saati’s method, which has a high degree of consistency of ratings for various experts
Summary
An analysis of the current state and further ways of development of the electric power industry shows that its reformation is carried out in a number of contradictions between economic relations and operation reliability of electric power system [1]. At the first stage, justified allocation of expenses for units and groups of electrical equipment is carried out taking into account the complex of operational factors, in particular, the emergency risk This approach provides a rational allocation of costs within the allocated repair and maintenance fund. Characteristics, taking into account the most important operational factors, the impact of out of service and replacement of electrical equipment on EPS reliability This requires the development of the optimal distribution methodology of costs between groups of the same type of electrical equipment of the energy company, taking into account the most important operational factors. Solving this problem is possible through EPS complex simulation
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