Solving power system unit commitment with wind farms using multi-objective quantum-inspired binary particle swarm optimization

2013 ◽  
Vol 5 (2) ◽  
pp. 021410 ◽  
Author(s):  
Xiaoshan Wu ◽  
Buhan Zhang ◽  
Junfang Li ◽  
Gang Luo ◽  
Yao Duan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Unit Commitment ◽  
Particle Swarm ◽  
Wind Farms ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Multi Objective

scholarly journals Operation cost reduction in unit commitment problem using improved quantum binary PSO algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 1149 ◽  
Author(s):  
Ali Nasser Hussain ◽  
Ali Abduladheem Ismail
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Cost Reduction ◽  
Unit Commitment ◽  
Particle Swarm ◽  
Total Power ◽  
Binary Particle Swarm Optimization ◽  
Unit Commitment Problem ◽  
Swarm Optimization ◽  
Operation Cost

Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC is used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm.

scholarly journals A Novel Multi-Objective Discrete Particle Swarm Optimization with Elitist Perturbation for Reconfiguration of Ship Power System

2017 ◽  
Vol 24 (s3) ◽  
pp. 79-85
Author(s):  
Lingjie Zhang ◽  
Jianbo Sun ◽  
Chen Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm ◽  
Index Test ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Shipboard Power System ◽  
Shipboard Power

Abstract A novel multi-objective discrete particle swarm optimization with elitist perturbation strategy (EPSMODPSO) is proposed and applied to solve the reconfiguration problem of shipboard power system(SPS). The new algorithm uses the velocity to decide each particle to move one step toward positive or negative direction to update the position. An elitist perturbation strategy is proposed to improve the local search ability of the algorithm. Reconfiguration model of SPS is established with multiple objectives, and an inherent homogeneity index is adopted as the auxiliary estimating index. Test results of examples show that the proposed EPSMODPSO performs excellent in terms of diversity and convergence of the obtained Pareto optimal front. It is competent to solve network reconfiguration of shipboard power system and other multi-objective discrete optimization problems.

scholarly journals A binary particle swarm optimization approach for power system security enhancement

2022 ◽  
Vol 12 (2) ◽  
pp. 1929
Author(s):  
Padmanabha Raju Chinda ◽  
Ragaleela Dalapati Rao
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Particle Swarm ◽  
System Security ◽  
Binary Particle Swarm Optimization ◽  
Optimization Approach ◽  
Power System Security ◽  
Swarm Optimization

Improvement of power system security manages the errand of making healing move against conceivable system overloads in the framework following the events of contingencies. Generation re-dispatching is answer for the evacuation of line overloads. The issue is the minimization of different goals viz. minimization of fuel cost, minimization of line loadings and minimization of overall severity index. Binary particle swarm optimization (BPSO) method was utilized to take care of optimal power flow issue with different targets under system contingencies. The inspiration to introduce BPSO gets from the way that, in rivalry with other meta-heuristics, BPSO has demonstrated to be a champ by and large, putting a technique as a genuine alternative when one needs to take care of a complex optimization problem. The positioning is assessed utilizing fuzzy logic. Simulation Results on IEEE-14 and IEEE-30 bus systems are presented with different objectives.

Analysis of Micro Array Gene Expression Data Using Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSO-FWC) Technique

2020 ◽  
Vol 10 (5) ◽  
pp. 1049-1056
Author(s):  
M. Kalaiarasu ◽  
J. Anitha
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Expression Data ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Weighted Clustering

In the rapidly advancing field of genomics, microarray technologies have turned into a ground-breaking system on simultaneous monitoring the expression patterns of multiple genes under various arrangements of constraints. A fundamental errand is to propose diagnostic techniques to distinguish cluster of genes comparative expression designs and are initiated by comparative conditions. And furthermore, the relating investigation has issue is to cluster multi-condition gene expression data. To overcome these issues, the vast measure of data obtained by this technology, resort to clustering methods that distinguish clusters of genes of share similar expression profiles. The motivation of this work is to introduce a clustering method in microarray gene expression data analysis. Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSOFWC) algorithm is proposed to analyze gene expression data. In high dimensionality, a quick heuristic based pre-processing technique is employed to diminish some of the basic domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is implemented in MATLAB tool used for finding further feature subsets. The investigative are directed to distinguish the execution of the proposed work with existing clustering approaches.

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