Power System Reliability Assessment— A Review on Analysis and Evaluation Methods

Author:

GARİP Selahattin1,ÖZDEMİR Şaban1,ALTIN Necmi2

Affiliation:

1. GAZI UNIVERSITY

2. GAZI UNIV

Abstract

The structures of power systems and their capacity have been updated significantly from time to time. Therefore, a reliability analysis is an essential issue in the planning, designing, and operation of electric power systems. Thus, a number of methods have been proposed. These are grouped as analytical based methods, simulation-based methods, and hybrid methods. Some methods like Monte Carlo, Markov etc., developed and some indices such as interruption indices, energy-oriented indices, are used for the evaluation. The purpose of this review study is to investigate the reliability analysis approaches, methods and difficulties, and to report importance of the reliability analysis in power systems. Therefore, reliability indices and evaluation methods and models of evaluation of power system are listed and explained. Besides, modeling and computational burden and complexity and problems are discussed. The importance of reliability analysis for emerging power systems is examined and explained.

Publisher

Journal of Energy Systems

Subject

Management, Monitoring, Policy and Law,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A machine learning-driven support vector regression model for enhanced generation system reliability prediction;COMPEL - The international journal for computation and mathematics in electrical and electronic engineering;2023-11-29

2. An Efficient Method for the Reliability Evaluation of Power Systems Considering the Variable Photovoltaic Power Output;Applied Sciences;2023-08-08

3. Reliability Analysis of Microgrids: Evaluation of Centralized and Decentralized Control Approaches;Electric Power Components and Systems;2023-07-10

4. Maximizing Network Reliability in Large Scale Infrastructure Networks: A Heat Conduction Model Perspective;Proceedings of the 2023 15th International Conference on Machine Learning and Computing;2023-02-17

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