Intelligent Particle Swarm Optimization of Superconducting Magnetic Energy Storage devices

2014 ◽  
Author(s):  
Ruxandra Barbulescu ◽  
Aurel-Sorin Lup ◽  
Gabriela Ciuprina ◽  
Daniel Ioan ◽  
A. Egemen Yilmaz
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Magnetic Energy ◽  
Particle Swarm ◽  
Energy Storage Devices ◽  
Swarm Optimization ◽  
Storage Devices ◽  
Superconducting Magnetic Energy Storage ◽  
Magnetic Energy Storage

SimPower-based analysis and design of a hybrid wind–diesel-superconducting magnetic energy storage system for simultaneous frequency and voltage control

2019 ◽  
Vol 43 (6) ◽  
pp. 596-608 ◽  
Author(s):  
Aaqib Ali Abass ◽  
Mairaj Ud-Din Mufti
Keyword(s):  
Energy Storage ◽  
Magnetic Energy ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Storage Devices ◽  
Real Power ◽  
Analysis And Design ◽  
Average Value ◽  
Superconducting Magnetic Energy Storage ◽  
Magnetic Energy Storage

Power quality control in a stand-alone power system is a demanding task. For satisfactory operation, such systems are being augmented with fast-acting energy storage devices. In this article, a stand-alone wind–diesel system augmented with a small-rating superconducting magnetic energy storage system is considered for both reactive and real power balance. Suitable controllers are proposed which force the superconducting magnetic energy storage system to exchange both reactive and real power with the system under various perturbations. A simulation platform is developed in SimPower to virtually validate the system model and control design aspects. Superconducting magnetic energy storage system and its power electronic interface are represented by average value models and the various controller parameters are tuned using Genetic Algorithm. Simulation results show that both voltage and frequency of the system are improved.

scholarly journals A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8575
Author(s):  
Rehan Ali Khan ◽  
Shiyou Yang ◽  
Shafiullah Khan ◽  
Shah Fahad ◽  
Kalimullah
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Magnetic Energy ◽  
Particle Swarm ◽  
Optimal Solution ◽  
High Dimensional ◽  
Adaptive Mutation ◽  
Dynamic Factor ◽  
Energy Storage Devices ◽  
Swarm Optimization

Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.

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