A Holistic Framework for Asset Integrity Management in Power Distribution Systems: Dynamic Risk Assessment and AI-Enabled Decision Support
TL;DR
The proposed AIA&C framework offers a structured means of integrating asset integrity, reliability, risk, and maintenance considerations while providing a foundation for the future development of AI-based asset management solutions for utility applications.
Abstract
Asset integrity is essential for sustaining the reliable operation of power distribution systems. This paper proposes an asset integrity assessment and control (AIA&C) framework that integrates artificial intelligence (AI)-enabled capabilities with risk and reliability methodologies to support asset integrity management decisions. The framework consists of ten interrelated phases covering stakeholder requirements, regulatory compliance, value-based decision-making, condition monitoring, reliability evaluation, risk assessment, maintenance strategy selection, asset life-cycle management, environmental considerations and continuous improvement in a unified decision support structure. Within the framework, risk and reliability evaluations are performed using a Python-based application that generates a dynamic risk index for maintenance decision support. The proposed approach combines thermal condition-based probability of failure (PoF) estimation with consequence of failure (CoF) assessment to quantify asset risk. The PoF model incorporates thermal anomaly data and reliability principles, whereas the CoF assessment accounts for economic, operational, and safety-related impacts. Although the proposed approach offers a systematic means of quantifying risk, additional validation using historical failure records is necessary before statistically validated probabilistic predictions can be confirmed. The framework enables structured assessment of asset condition, reliability, and risk to support maintenance planning and asset management activities. A case study involving a distribution transformer is presented to demonstrate the implementation of the dynamic risk-index methodology and the application of the mathematical formulations included within the framework. The case study combines PoF with cost, downtime, and safety metrics to determine CoF, producing a real-time four-dimensional (4D) risk matrix that supports maintenance prioritization and the selection of predefined reliability-centered maintenance actions. The findings indicate the potential of the proposed dynamic risk-index methodology to support risk-informed maintenance decisions in power distribution systems. The proposed AIA&C framework offers a structured means of integrating asset integrity, reliability, risk, and maintenance considerations while providing a foundation for the future development of AI-based asset management solutions for utility applications.