Stochastic Modelling of Weather-Related Transmission Line Outages-Reference-Cited by-同舟云学术

Stochastic Modelling of Weather-Related Transmission Line Outages

Published:2020-01 Issue:1 Volume:11 Page:66-82
ISSN:1947-9328
Container-title:International Journal of Operations Research and Information Systems
language:en
Short-container-title:

Author:

Sony Michael¹^ORCID,Chettiar Mariappan Veerachamy²

Affiliation:

1. Namibia University of Science and Technology, Windhoek, Namibia

2. Agnel Institute of Technology and Design, Bardez, India

Abstract

The physical environment around transmission lines plays a major role in the resulting reliability of the power network. The inclusion of weather in the failure and repair process will lead to realistic modelling of the power network. This article suggests a modelling methodology to take into account weather-related failures. Besides a maintenance management strategy using dynamic programming, it is suggested to minimizing the cost of maintenance while accounting for weather-related failures. The data obtained from 220kV Transmission lines from Goa, India, is used to stochastically model the phenomenon. A three-state weather model is suggested, and accordingly the failure and repair phenomenon are segregated and stochastically modelled. Time-varying expressions for computing the availability in each weather condition is computed. This model can be used by the power utilities to realistically model weather-related failures.

Publisher

IGI Global

Subject

Information Systems and Management,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Information Systems,Management Information Systems

Reference24 articles.

1. Modeling dependent failures for the availability of extra high voltage transmission lines

2. Weather-based distribution system reliability evaluation

3. Application of adverse and extreme adverse weather: modelling in transmission and distribution system reliability evaluation

4. Optimum maintenance policy with Markov processes

5. Failure rate prediction under adverse weather conditions in an electric Distribution System using Negative Binomial Regression

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