NDLI: Machine-learning-based reliability evaluation framework for power distribution networks

Content Provider	IET Digital Library
Author	Li, Gengfeng Huang, Yuxiong Bie, Zhaohong Ding, Tao
Abstract	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.
Starting Page	2282
Ending Page	2291
Page Count	10
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 12, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1520
Journal	IET Generation, Transmission & Distribution
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-03-06
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Actual Distribution System Conventional Reliability Evaluation Method Distribution Network Efficient Reliability Evaluation Pattern Evaluation Accuracy Failure Feature Description Knowledge Engineering Technique Learning in AI Machine-learning Technique Machine-learning-based Reliability Evaluation Framework Monte Carlo Method Pattern Classification Power Distribution Network Power Distribution Reliability Power Distribution System Power System Reliability RBTS Bus 2 System Reliability Reliability Evaluation Algorithm Simulation-based Reliability Evaluation Method SSC-based System State Assessment State-Space Classification-based Method Supervised Learning Algorithm Named Perceptron System Reliability Telecommunication Network Reliability Topology Analysis Efficiency
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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