scholarly journals Optimal Overcurrent Relay Coordination Using Optimized Objective Function

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Seyed Hadi Mousavi Motlagh ◽  
Kazem Mazlumi
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Overcurrent Relays ◽  
Overcurrent Relay ◽  
Novel Strategy

A novel strategy for directional overcurrent relays (DOCRs) coordination is proposed. In the proposed method, the objective function is improved during the optimization process and objective function coefficients are changed in optimization problem. The proposed objective function is more flexible than the old objective functions because various coefficients of objective function are set by optimization algorithm. The optimization problem is solved using hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSOA). This method is applied to 6-bus and 30-bus sample networks.

scholarly journals Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

2019 ◽  
Vol 234 (1) ◽  
pp. 39-55
Author(s):  
Ziyang Li ◽  
Quan Zhou ◽  
Yunfan Zhang ◽  
Ji Li ◽  
Hongming Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Knowledge Based ◽  
Accelerated Particle Swarm Optimization ◽  
Fuzzy Knowledge

The self-adaptive and highly robust proportional-integral-like fuzzy knowledge–based controller has been developed to regulate air–fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge–based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air–fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge–based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge–based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller.

scholarly journals Combination of Intermittent Search Strategy and an Improve Particle Swarm Optimization algorithm (IPSO) for damage detection of steel frame

2021 ◽  
Vol 16 (59) ◽  
pp. 141-152
Author(s):  
Cuong Le Thanh ◽  
Thanh Sang-To ◽  
Hoang-Le Hoang-Le ◽  
Tran-Thanh Danh ◽  
Samir Khatir ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Search Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Search Speed ◽  
Eagle Strategy

Modality and intermittent search strategy in combination with an Improve Particle Swarm Optimization algorithm (IPSO) to detect damage structure via using vibration analysis basic principle of a decline stiffness matrix a structure is presented in the study as a new technique. Unlike an optimization problem using a simplistic algorithm application, the combination leads to promising results. Interestingly, the PSO algorithm solves the optimal problem around the location determined previously. In contrast, Eagle Strategy (ES) is the charging of locating the position in intermittent space for the PSO algorithm to search locally. ES is easy to deal with its problem via drastic support of Levy flight. As known, the PSO algorithm has a fast search speed, yet the accuracy of the PSO algorithm is not as good as expected in many problems. Meanwhile, the combination is powerful to solve two problems: 1) avoiding local optimization, and 2) obtaining more accurate results. The paper compares the results obtained from the PSO algorithm with the combination of IPSO and ES for some problems and between experiment and FEM to demonstrate its effectiveness. Natural frequencies are used in the objective function to solve this optimization problem. The results show that the combination of IPSO and ES is quite effective.

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