كنفرانس بين المللی مهندسی صنايع و سيستم ها

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

Artificial Intelligence for Asset Management in Electricity Distribution Networks: From Predictive Maintenance to Intelligent Decision Support

نویسندگان :

Mohsen Kojury-naftchali¹

1- شرکت توزیع نیروی برق مازندران

کلمات کلیدی :

Artificial intelligence،asset management،condition monitoring،electricity distribution network،predictive maintenance،machine learning

چکیده :

Electricity distribution networks are entering a period of rapid transformation driven by ageing infrastructure, increasing electricity demand, distributed energy resources and the digitalization of grid operations. Traditional asset-management practices based primarily on fixed maintenance schedules, equipment age, periodic inspection, and historical failure information are increasingly insufficient for managing large and heterogeneous asset populations. Artificial intelligence offers an opportunity to transform distribution asset management from reactive and time-based maintenance toward condition-based, predictive, and risk-informed decision-making. This paper presents a comprehensive review of the role of artificial intelligence in asset management of electricity distribution networks. The paper focuses on the application of machine learning and intelligent decision-support systems to distribution assets. Particular attention is given to distribution transformers, circuit breakers, and substation equipment. The paper discusses how AI can support condition monitoring, asset-health assessment, failure prediction, maintenance prioritization, remaining-life estimation and network resilience. The analysis shows that the greatest value of AI is not simply its ability to predict equipment failure, but its ability to connect asset condition, failure risk, network criticality, maintenance resources, and investment decisions within a unified asset-management process. The paper proposes a human-AI collaborative approach in which AI provides large-scale data analysis and decision support while engineers retain responsibility for validation and final decisions. The paper concludes that AI can become a fundamental technology for next-generation distribution asset management, provided that implementation is supported by appropriate data governance, cybersecurity, explainable models, engineering expertise, and clearly defined economic objectives.