A Hybrid Monte Carlo Simulation and Multi Label Classification Method for Composite System Reliability Evaluation Academic Article

abstract

• 1969-2012 IEEE. This paper presents a new approach for reliability evaluation of composite power systems by combining Monte Carlo simulation and multi label k-nearest neighbor (MLKNN) algorithm. MLKNN is a classification technique in which target vector of each instance is assigned into multiple classes. In this paper, MLKNN is used to classify states (failure or success at system or bus level) of a complete power system without requiring optimal power flow (OPF) analysis, except in the training phase. As a result, the computational burden to perform OPF is reduced dramatically. For illustration, the proposed method is applied to the IEEE 30 BUS Test System and IEEE Reliability Test System. The obtained results from various case studies demonstrate that MLKNN based reliability evaluation provides promising results in both classification accuracy and computation time in evaluating the composite power system reliability.

authors

• Singh, Chanan

published proceedings

• IEEE TRANSACTIONS ON POWER SYSTEMS

author list (cited authors)

• Urgun, D., & Singh, C.

citation count

• 40

complete list of authors

• Urgun, Dogan||Singh, Chanan

publication date

• March 2019

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE TRANSACTIONS ON POWER SYSTEMS  Journal

keywords

• Composite Power System Reliability Evaluation
• Knn Learning Algorithm
• Monte Carlo Simulation
• Multi Label Classification

Digital Object Identifier (DOI)

• 10.1109/TPWRS.2018.2878535

start page

• 908

end page

• 917

volume

• 34

issue

• 2

URL

• http://dx.doi.org/10.1109/tpwrs.2018.2878535