| With the development of the power industry, the scale of the power grid is getting larger and larger, the number of the power equipment is increasing sharply as well. In order to reduce the failure rate of the equipment and the maintenance cost, as well as increase safe operation level of power grid and power supply reliability, the maintenance system is turning from traditional schedule maintenance to condition based maintenance. While with the rapid development of the information technology and the application of big data in the power system, it provides new development opportunities and technology support to achieve the smarter and more scientific condition based maintenance. With the purpose to construct the risk based maintenance-assistant-decision system, this article is based on the panoramic real-time state data. This article mainly concentrates on the design of the general plane, forecast methods of the power transmission and transformation equipment's failure rate, cost calculation methods of the power transmission and transformation equipment.The main purpose of the risk based maintenance-assistant-decision system is to evaluate the operation risk cost and maintenance cost. Then the system provides the evidence to make scientific and reasonable maintenance strategy for the operation and maintenance department. According to the above application requirements, this article takes base data layer, risk cost analysis layer and visual exhibition layer as the framework, then proposes the hierarchical design schema of the system. Based on that, it analyses the data resource of the base data layer, the data exchange mechanism of CIM/XML; proposes the main function and requirement of the risk cost analysis; provides the present content and pattern of the risk based maintenance-assistant-decision system.The failure rate calculation has been one of the most difficult problems in the risk evaluation of the power transmission and transformation equipment. This article uses the Marquardt method to form the basic failure rate curve. Based on that, aimed at the main factors that influence the equipment's failure rate, it advances the modified approach of the basic failure rate curve concerning personal defect, maintenance, health state evaluation and other influence factors. Based on the modified failure rate curve, it constructs the failure rate analysis model of the power transmission and transformation equipment. The simulation results verify the effectiveness the modified approach provided by this article.The key to reduce risk costs is to prevent load outage caused by failure or maintenance of power transmission and transformation equipment. This paper lists main factors that influence short-term changes in power load, and provides mathematical approach to predict daily power load based on data collections of similar days. According to optimization theory, an optimal model of reducing power load under condition of power transmission and equipment outage is established, combined with equality and non-equality limitations of voltage and power, considering the load importance level. Moreover, the non-linear programming based on the AC power flow is also designed to provide ease of communications among peers. To test the effectiveness of resulted prediction method and model of reducing power load, a few case studies are also implied and carefully evaluated.Based on the above research, this paper further discusses the risk costs when power transmission and transformation equipment is either operating with unsolved technical issues or maintenance due to outage, more specifically through the levels of both equipment itself and the operation system. Moreover, the economic analysis then quantifies the direct and indirect loss of each key component. Altogether, it sums up with a mathematical method for calculating risk cost of operation and overhaul cost of power transmission and transformation equipment, which is then carefully evaluated through case studies.Lastly, the paper summarizes the key research points, and discusses the future research directions based on obtained results. |
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