Open AccessModified affine arithmetic based continuation power flow analysis for voltage stability assessment under uncertainty
Author(s) -
Adusumilli Bala Surendra,
Kalyan Kumar Boddeti
Publication year - 2018
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.2018.5479
Subject(s) - affine arithmetic , continuation , voltage , power flow , control theory (sociology) , stability (learning theory) , affine transformation , ac power , power (physics) , monte carlo method , algorithm , computer science , mathematics , mathematical optimization , electric power system , statistics , engineering , electrical engineering , physics , control (management) , quantum mechanics , artificial intelligence , machine learning , pure mathematics , programming language
Continuation power flow (CPF) analysis has been used in the literature to determine the voltage collapse point from active power versus voltage curves (PV curves) for steady‐state voltage stability assessment. Affine arithmetic‐based (AA) CPF analysis to determine PV curve bounds under uncertainty in power generation was introduced in the literature to overcome the problem of large computational time with Monte Carlo (MC) simulations, by getting a faster solution with a reasonably good accuracy. However, AA operations lead to more noise terms and hence overestimation of bounds. In the present work, a modified AA (modAA)‐based CPF analysis is proposed to determine PV curve bounds by considering uncertainties associated with active and reactive power injections at all buses in the system. The proposed method reduces the overestimation caused by the AA operations and gives more accurate solution bounds. The proposed modAA‐based CPF analysis is tested on 5‐bus test case, IEEE 57, European 1354 and Polish 2383‐bus systems. The simulation results with the proposed method are compared with MC simulations and AA‐based CPF analysis to show the efficacy of the proposed method.