Open AccessRobust meter placement for active distribution state estimation using a new multi‐objective optimisation model
Author(s) -
Prasad Sachidananda,
Vinod Kumar Dulla Mallesham
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2018.5175
Subject(s) - phasor , particle swarm optimization , phasor measurement unit , genetic algorithm , weibull distribution , metre , smart meter , reliability (semiconductor) , mathematical optimization , sorting , distributed generation , computer science , engineering , parametric statistics , control theory (sociology) , electric power system , smart grid , algorithm , mathematics , power (physics) , renewable energy , statistics , electrical engineering , physics , astronomy , control (management) , quantum mechanics , artificial intelligence
In this study, a new multi‐objective optimisation model is developed to deploy phasor measurement units (PMUs) and intelligent electronic devices (IEDs) optimally in an active distribution network for state estimation. Three objective functions are considered in this optimisation problem such as the total cost of PMUs and IEDs, and the root mean square value of state estimation error. Since conflicting objectives are considered; to find the optimal locations of the devices, the meter placement problem is developed in a multi‐objective framework to get compromised solution. The best compromised solution is obtained using multi‐objective hybrid particle swarm optimisation (PSO)‐Krill Herd algorithm (KHA). Furthermore, the reliability of the proposed meter placement technique is tested under load and generators’ output variations. All distributed generations (DGs) are considered as wind generators and the output of each DG is modelled using Weibull distribution function. The proposed algorithm is tested on IEEE 69‐bus system as well as on a practical Indian 85‐bus system. The obtained results have been compared with traditional PSO, KHA and also with well known non‐dominated sorting genetic algorithm.