access icon openaccess Machine-learning-based reliability evaluation framework for power distribution networks

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 17/10/2019, Accepted 06/02/2020, Revised 02/02/2020, Published 06/03/2020

Reliability evaluation of power distribution systems is a well-studied and understood problem, but topology analysis efficiency and failure feature description are still limiting the practical application of reliability evaluation in distribution systems. One approach the authors proposed in this study is to incorporate the machine-learning technique into the simulation-based reliability evaluation method, and assess the system reliability in an empirical fashion. In this study, a framework for performing the machine-learning-based reliability evaluation, and corresponding modelling processes are first established. Then, by inducing a supervised learning algorithm named perceptron, a state-space classification-based (SSC) method for system state assessment, the core procedure of reliability evaluation, is proposed. On this basis, a reliability evaluation algorithm combining the SSC-based system state assessment and sequential Monte Carlo simulation is proposed, where the workload of topology analysis required in conventional reliability evaluation methods can be released. Furthermore, extensive case studies are conducted on the Roy Billinton Test System (RBTS) bus 2 system and an actual distribution system to verify the proposed models and algorithms. Results show that the proposed framework supports a more efficient reliability evaluation pattern while ensuring the evaluation accuracy.

Inspec keywords: Monte Carlo methods; power distribution reliability; power system reliability; reliability; learning (artificial intelligence); telecommunication network reliability; pattern classification

Other keywords: evaluation accuracy; machine-learning-based reliability evaluation framework; supervised learning algorithm named perceptron; reliability evaluation algorithm; conventional reliability evaluation methods; efficient reliability evaluation pattern; failure feature description; state-space classification-based method; topology analysis efficiency; system reliability; power distribution systems; machine-learning technique; simulation-based reliability evaluation method; power distribution networks; actual distribution system; SSC-based system state assessment; RBTS bus 2 system

Subjects: Knowledge engineering techniques; Reliability; Distribution networks

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