Study On Selection And Prediction Of Original Reliability Parameter In Power System

The analysis and processing of reliability original parameters is the most basic and most important work in power system reliability assessment, and the authenticity and validity of power system reliability assessment depends on the accuracy of reliability original parameters. However, the study of reliability original parameters is relatively lacking in the power system reliability research, so the research of reliability original parameters is very important to improve the credibility of reliability assessment results and to guide the design and operation of power systems.Firstly, the paper analyzed the development and importance of power system reliability research and the significance of reliability original parameters research, and discussed the classification, sources, characteristic, and analysis methods of reliability original parameters. Secondly, the selection of transmission line failure rate based on the fuzzy differences degree and prediction of reliability original parameters based on optimized GM (1,1) were mainly studied. Finally, the framework of reliability management information system was designed.Taking into account the lack of reliability original parameters in the new electrical engineering reliability assessment, this paper proposed a method on the selection of transmission line failure rate with fuzzy difference degree. Based on the Grey and Fuzzy math theory, with weather conditions as the impact factor, the fuzzy differences degree was formed by combinating the gray relational grade and fuzzy distance to select failure rate of new transmission line projects. This proposed method had a higher precision than the gray relational grade or fuzzy nearness used merely. This method was a effective method to select the power system reliability original parameters, and it can not only avoid duplication of data statistics, but can also make the reliability assessment of new projects have theoretical basis and credible assessment.In order to increase the volume of original data, the paper used the optimized GM (1,1) model to predict the reliability original parameters. The proposed model is optimized by integrating prediction values with fuzzy nearness as weight coefficient on the basis of using least squares to optimize the initial value. The fuzzy nearness between original series and fitting series are obtained by fitting original parameters of different time. This model can fully exploit the information contained in the original parameters to overcome the defect of the prediction precision decline with prediction point of traditional GM (1,1) model.The current reliability management system has defects:the reliability data with manual input was inefficient, there was data duplication between reliability management system and other systems, the system was lack of advanced application function. This paper designed a reliability management system framework. The designed system realized such functions as reliability objective management, reliability micro-management, reliability analysis on scheduling outrage management, reliability analysis on effect and maintenance management, reliability theoretical calculations based on the reliability index statistics and the calculation of equipment basic parameters. The proposed system can avoid data duplication and ensure the accuracy and completeness of reliability data, so can provide credible parameters for the advanced reliability application modules, and the advanced applications will has substantive significance on the power network planning, construction, maintenance and operation...