AIFeb 13, 2013

Supply Restoration in Power Distribution Systems - A Case Study in Integrating Model-Based Diagnosis and Repair Planning

arXiv:1302.3608v148 citations
Originality Synthesis-oriented
AI Analysis

This work addresses a critical problem for electricity distributors by providing a practical solution for supply restoration, though it appears incremental as it builds on existing methods for diagnosis and planning.

The paper tackles the challenge of integrating model-based diagnosis and repair planning for supply restoration in faulty power distribution systems, where partial observability and stochastic repair actions complicate the process, and it describes a pragmatic approach to address these difficulties.

Integrating diagnosis and repair is particularly crucial when gaining sufficient information to discriminate between several candidate diagnoses requires carrying out some repair actions. A typical case is supply restoration in a faulty power distribution system. This problem, which is a major concern for electricity distributors, features partial observability, and stochastic repair actions which are more elaborate than simple replacement of components. This paper analyses the difficulties in applying existing work on integrating model-based diagnosis and repair and on planning in partially observable stochastic domains to this real-world problem, and describes the pragmatic approach we have retained so far.

Foundations

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