كنفرانس بين المللی مهندسی صنايع و سيستم ها

صفحه اصلی / دوازدهمین كنفرانس بين المللی مهندسی صنايع و سيستم ها

Risk-Based Decision Making for Maintenance Optimization in Electricity Distribution Networks: A Reliability-Centered Asset Management Framework

نویسندگان :

Mohsen Kojury-naftchali¹ Abdollah Hoseinzadeh² Mohammad Shams³ Esmaeel Nabavi⁴

1- شرکت توزیع نیروی برق مازندران 2- شرکت توزیع نیروی برق مازندران 3- شرکت توزیع نیروی برق مازندران 4- شرکت توزیع نیروی برق مازندران

کلمات کلیدی :

Risk-Based Maintenance; Management; ; ; ; ; .،Reliability-Centered Maintenance،Distribution System Reliability،Maintenance Optimization،Failure Probability،Reliability Engineering

چکیده :

Electricity distribution network is vital infrastructures that directly influence the reliability, security, and economic performance of modern power systems. Aging assets, increasing electricity demand, and constrained maintenance budgets have significantly increased the complexity of maintenance planning within distribution utilities. Traditional maintenance approaches, often fail to prioritize limited maintenance resources according to the actual operational risk associated with network assets. This paper presents a comprehensive risk-based decision-making framework for maintenance optimization in electricity distribution networks. The proposed framework combines asset condition assessment, probabilistic failure modeling, consequence analysis, and multi-criteria decision-making techniques to determine maintenance priorities for critical network assets. The methodology integrates Failure Mode and Effects Analysis (FMEA), risk matrices, reliability-centered maintenance principles, and optimization methods to support maintenance scheduling under limited financial resources. Simulation results on a distribution network case study illustrate that risk-based maintenance significantly improves system reliability by reducing expected interruption duration, minimizing maintenance costs, increasing asset utilization, and enhancing decision transparency compared with conventional maintenance strategies.