Analysis of operational risks of electricity distribution network based on the fuzzy cognitive map approach
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
One of the most essential development factors in any country is the quality of electricity supply and distribution. Energy consumers seek electricity supply at a very high safety level. Considering the sensitivity of electronic devices and the dependence of most activities on electricity, providing sustainable energy in urban systems is critical. Unscheduled shutdowns are the leading cause of disruption in the continuity of electricity supply and reduce the quality of power delivered to customers. Operational risks are potential events that result in unplanned outages in the network. In the current research, the fuzzy cognitive map (FCM) method has been used to investigate the relationships between risks and to reach a comprehensive solution for the simultaneous control and management of several risks. Accordingly, extracting the effect of operational risks on each other and how to draw them in the form of a FCM is analyzed and represented. The results emphasize that adverse weather conditions are the most influential with the highest degree of output and equipment failure is the most influential with the highest degree of input in operational risks. The highest value of the sum of input and output degrees (centrality) is the breakdown in the transformer, which has the highest value. The analysis of managerial and practical perspectives shows that the operators, by focusing on forecasting weather conditions and retrofitting network structures and technical management of transformers, reach a convergent and sustainable solution to manage operational risks and ultimately reduce unplanned shutdowns.
Language:
English
Published:
Journal of Energy Management and Technology, Volume:8 Issue: 4, Autumn 2024
Pages:
343 to 348
https://www.magiran.com/p2828078
سامانه نویسندگان
مقالات دیگری از این نویسنده (گان)
-
Analyzing and Forecasting of Coronavirus Time-Series Data: Performance Comparison of Machine Learning and Statistical Models
Majid Alimohammadi Ardekani, Mohammadhossein Karimi-Zarchi, *
Journal of Advances in Industrial Engineering, Summer and Autumn 2024 -
A novel approach to multi-objective portfolio selection: modeling emerging financial markets using satisfaction functions and fuzzy values
Milad Saleki, Mohamad Saber Falah Nejad *, , Mohammad Aref Dehghani Tafti
Journal of Decisions and Operations Research,