Failure rate modeling: A non-parametric data mining approach to MV network field data
ABSTRACT Power distribution fault statistics provides valuable knowledge of system failure rate behavior, as the start point of reliability evaluations. Using this statistics, electric utilities trace and develop their reliability plans based on fault statistics. This paper considers a data mining approach to model momentary failure rate in terms of the most influential factors. A methodology is presented here, for momentary fault cause identification, using a feature selection algorithm applied to MV network of the Greater Tehran Electric Distribution Company (GTEDC). Subsequently, two non-parametric failure rate models; classification and regression tree (CART) and artificial neural network (ANN) are utilized to cope with the high non-linearity of the problem space. Results and comparisons of the characteristics of the proposed methods illustrate the advantages and disadvantages of each model.
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ABSTRACT: Power distribution fault statistics provide splendid resource for extracting experimental knowledge. The extracted knowledge includes the inherit characteristics of the network assets. Analysis and estimation of failures require a comprehensive understanding of faults in terms of the relevant effective parameters. This paper outlines a data-driven model to represent momentary failure rate in terms of the most influential factors based on the study of the recorded historical fault data as well as the expert's experiments in the Greater Tehran Electricity Distribution Company. A methodology is presented for momentary fault causes identification and model construction using artificial neural networks. Satisfactory results indicate that the developed model can easily be implemented to estimate other fault types in power distribution systems.01/2009;
Conference Paper: Advanced reliability analysis for distribution network[Show abstract] [Hide abstract]
ABSTRACT: One of the most challenging tasks for network utilities is to reduce costs and improve reliability at the same time. In order to manage the task successfully advanced IT-solutions are needed providing reliability analysis as part of the planning process. This paper presents advanced reliability analysis for distribution network. A sophisticated failure rate model has been developed based on failure statistics of the network and engineer judgement. Practical fault management process and statistics has been analyzed in order to model the customer outage times in different fault situations. Outage cost model for different customer groups are used in order to reach financial results by the analysis. The practical implementation and some demonstration studies are presented in the end of the paper.Electric Utility Deregulation, Restructuring and Power Technologies, 2004. (DRPT 2004). Proceedings of the 2004 IEEE International Conference on; 05/2004