Open AccessReliability analysis of phasor measurement unit incorporating hardware and software interaction failures
Author(s) -
Roy Diptendu Sinha,
Murthy Cherukuri,
Mohanta Dusmanta Kumar
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.0115
Subject(s) - reliability engineering , phasor , reliability (semiconductor) , phasor measurement unit , computer science , software , units of measurement , unit (ring theory) , embedded system , engineering , electric power system , power (physics) , operating system , mathematics , physics , quantum mechanics , mathematics education
As the electrical power system has increased in its geographical sprawl, adequate measures for reliability analysis for the wide area measurement system (WAMS) and phasor measurement units (PMUs) have become necessary. However, existing PMU reliability models are constrained by the assumption that PMU failures may be encountered either because of hardware failures or because of software failures only. Most modem safety critical systems, like the PMU are characterised by close proximity of hardware and software operations which leads to correlated failures. This is referred to as hardware–software interaction failure and is disregarded by contemporary PMU reliability models. In this paper, a modelling framework has been developed using Markov process that captures hardware–software interaction failures, apart from the hardware specific and software specific failures, and presents a Markov model‐based unified PMU reliability model. This paper also offers a novel Monte Carlo simulation (MCS) technique to estimate PMU failure data to account for scanty PMU failure data from field installations. The novel algorithms for MCS based PMU failure estimation are expounded along with detailed methodologies for fitting the simulated failure data to the unified reliability model. The results presented herein demonstrate the improved accuracy of the proposed method.