NDLI: Multi-objective hybrid decomposition and local dominance based meter placement for distribution system state estimation

Content Provider	IET Digital Library
Author	Chintala, Bhanu Prasad Kumar, D. M. Vinod
Abstract	The study proposes a new hybrid multi-objective evolutionary optimisation algorithm based on decomposition and local dominance for meter placement in distribution system state estimation. The evenly distributed qualitative and diverse solutions on the Pareto front are required for a decision-maker for selecting a final optimal solution. Such a Pareto front can be achieved by obtaining the balance between convergence and diversity of multi-objective optimisation algorithm. Therefore, the proposed method combined dominance and decomposition techniques, modelled meter placement as a constrained combinatorial multi-objective optimisation. The meter placement is designed as a trade-off between three objectives that are minimising the cost of the meters, average relative percentage error (ARPE) of voltage magnitude and ARPE of voltage angle. As the meter placement problem is a combinatorial optimisation, the binomial distribution-based Monte Carlo method is utilised to initialise the population, which aims to improve the diversity, as a consequence it improves the convergence, which is a by-product of this method. The results of the proposed method are compared with multi-objective evolutionary algorithm based on decomposition, non-dominated sorting genetic algorithm-II and with multi-objective hybrid particle swarm optimisation-krill herd algorithm, multi-objective hybrid estimation of distribution algorithm-interior point method and demonstrated on PG&E 69-bus distribution system and Practical Indian 85-bus distribution system.
Starting Page	4416
Ending Page	4425
Page Count	10
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 20, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/20
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2020.0294
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-07-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ARPE Average Relative Percentage Error Binomial Distribution-based Monte Carlo Method Combinatorial Optimisation Combined Dominance Constrained Combinatorial Multiobjective Optimisation Distribution Algorithm-interior Point Method Distribution System State Estimation Evenly Distributed Qualitative Solutions Genetic Algorithm Hybrid Multiobjective Evolutionary Optimisation Algorithm Indian 85-bus Distribution System Local Dominance Based Meter Placement Meter Placement Problem Modelled Meter Placement Monte Carlo Method Multiobjective Hybrid Decomposition Multiobjective Hybrid Estimation Multiobjective Hybrid Particle Swarm Optimisation-krill Herd Algorithm Nondominated Sorting Genetic Algorithm-II Optimal Solution Optimisation Technique Pareto Front Pareto Optimisation Particle Swarm Optimisation PG&E 69-bus Distribution System Power Engineering Computing Power System Economics Power System Managemen Power System Operation Power System State Estimation
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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