Paper Info
Title
An insight to the performance of estimation of distribution algorithm for multiple line outage identification.
Abstract
Realtime information relating to line outages has significant importance to pre-empt against the power system blackouts. Realtime information can be obtained by using phasor measurement units (PMUs) facilitating the realtime synchronized observations of voltage and current phasors at buses being monitored. Different optimization formulations including but not limited to linear, integer, stochastic, mixed integer and NP hard combinatorial optimization have been used to manipulate these phasor measurements for the detection of line outages. Single and double line outages can be addressed using combinatorial optimization but these are infeasible to apply for the detection of multiple line outages as the increased number of lines increases computational complexity. To alleviate the exponentially increased complexities of these combinatorial optimization problems, while investigating for multiple line outage, evolutionary, Estimation of Distribution Algorithm is used. This method gives near optimal solution in which computational complexity and time is reduced efficiently. In this paper we scrutinize the use of phasor angle measurements to detect multiple power line outages. The proposed EDA is compared with binary particle swarm optimization (BPSO) algorithm, adaptive BPSO and genetic algorithm (GA) in terms of line outage detection performance, fitness convergence w.r.t. iterations and time consumption. The simulation results depict that the proposed EDA outperforms the other state of the art algorithms.
Year
DOI
Venue
2018
10.1016/j.swevo.2017.09.006
Swarm and Evolutionary Computation
Keywords
Field
DocType
Estimation of Distribution Algorithm,Multiple Line Outage Detection,Smart Grid
Convergence (routing),Mathematical optimization,Units of measurement,Estimation of distribution algorithm,Computer science,Phasor,Electric power system,Combinatorial optimization,Genetic algorithm,Computational complexity theory
Journal
Volume
ISSN
Citations 
39
2210-6502
1
PageRank 
References 
Authors
0.34
15
6
Name
Order
Citations
PageRank
Ashfaq Ahmed1348.91
Q. Khan210.34
Muhammad Naeem348874.69
Muhammad Iqbal411.36
Alagan Anpalagan51263125.52
Muhammad Awais Azam617824.45