2017
DOI: 10.4186/ej.2017.21.6.101
|View full text |Cite
|
Sign up to set email alerts
|

Particle Swarm Optimization Based Equivalent Circuit Estimation for On-Service Three-Phase Induction Motor Efficiency Assessment

Abstract: Abstract. This paper presents the particle swarm optimization based equivalent circuit estimation (PSOBECE) method for three-phase induction motor efficiency analysis, during on-service condition. The prominent point of the paper is to accurately estimate the three-phase induction motor efficiency without disturbing motor operation, using basic electrical instruments, which are clamp-on power meter and non-contact tachometer. The proposed method estimates the parameters of induction motor equivalent circuit (E… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
6
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(6 citation statements)
references
References 9 publications
0
6
0
Order By: Relevance
“…where w is the inertia weight, 1 c and 2 c are the acceleration coefficient, 1 r and 2 r are the random constants in range   0.1 , g is the repetitions number [17]. The update of inertia weights [18] are defined by Equation 21 is the maximum value of multiple loop of the PSO algorithm, max w is respectively the largest of inertial weight sand and min w are respectively the smallest of inertial weights.…”
Section: Fuzzy Particle Swarm Optimization Design For Dynamic Positiomentioning
confidence: 99%
“…where w is the inertia weight, 1 c and 2 c are the acceleration coefficient, 1 r and 2 r are the random constants in range   0.1 , g is the repetitions number [17]. The update of inertia weights [18] are defined by Equation 21 is the maximum value of multiple loop of the PSO algorithm, max w is respectively the largest of inertial weight sand and min w are respectively the smallest of inertial weights.…”
Section: Fuzzy Particle Swarm Optimization Design For Dynamic Positiomentioning
confidence: 99%
“…A population consisting of particle is put into the n-dimensional search space with randomly chosen velocities and the initial location of particles [9,10]. The population of particles is expected to have high tendency to move in high dimensional search spaces in order for detecting better solution [11].…”
Section: Fuzzy-pso Controller Design 31 Particle Swarm Optimizationmentioning
confidence: 99%
“…finally, the best location for the whole group is performed by (10) as In order to discover the optimal parameters for fuzzy controller, each variable of the membership function for Ke(t) and Kde/dt control input is attributed to a particle. So, variables are initiated randomly in the search space to aim the optimal parameter for the fuzzy controller, which defines according to the fitness function (minimizing error) [15,16].…”
Section: Fuzzy-pso Controller Design 31 Particle Swarm Optimizationmentioning
confidence: 99%
See 1 more Smart Citation
“…The artificial methods take the artificial intelligent concept as their basis. The methods included in this group are, among others: Genetic Algorithm (GA) [8], Particle Swarm Optimization (PSO) [9]- [11], Firefly Algorithm (FA) [12], Flower Pollination Algorithm (FPA) [13], Artificial Neural Network (ANN) [14], Thunderstorm Algorithm (TA) [15], Fuzzy Algorithm [16], Coulomb's and Franklin's Algorithm [17], Whale Optimization Algorithm (WOA) [18], [19], and Simulated Annealing (SA) Algorithm [20].…”
Section: Introductionmentioning
confidence: 99%