Open AccessMulti‐objective optimisation of step voltage regulator operation and optimal placement for distribution systems design using linkage combination update‐non‐dominated sorting genetic algorithm‐II
Author(s) -
Shigenobu Ryuto,
Noorzad Ahmad Samim,
Yona Atsushi,
Senjyu Tomonobu
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.2016.1361
Subject(s) - blackout , transformer , voltage , sorting , electric power system , computer science , genetic algorithm , voltage regulator , algorithm , power (physics) , mathematical optimization , engineering , electrical engineering , mathematics , physics , quantum mechanics , machine learning
This study proposes the application of combinatorial multi‐objective optimisation (MOO) in an electrical power distribution system. Conventional electrical power systems do not consider reverse power flow, in which the power flows toward the feeder in the distribution system. However, reverse power flow toward the substation transformer is caused by voltage deviation with high penetration of distributed generators into a distribution system. Consequently, this causes faults in electric devices and may even lead to a massive blackout. To resolve voltage deviation problems, it is necessary to consider some trade‐offs. With this background, this study reveals three points. The first and second contributions regard general engineering research issues such as the definition of a new optimisation problem framework. To solve the problems discussed in this study, a new method of MOO was required. This method of MOO is applied to the power system to minimise voltage deviation while simultaneously minimising the number of required voltage control devices and operation. In addition, a new MOO method to determine the optimal placement of control devices while retaining operation diversity is proposed. Finally, each optimisation method is compared with numerical simulation and the advantages are summarised from the simulation results.