Operation Cost Minimization of Micro Grid using Particle Swarm Optimizer and Eagle Strategy Micro Grid's Operation Cost Minimization using PSO and ES

Author(s):  
Santhosh Kasi ◽  
R. Neela
Author(s):  
M. Gnanaprakash

As a result of rapid financial development and natural disasters, energy efficiency research, and high-quality electricity alternative energy options, as well as efficient electricity sources. In particular, the use of green energy sources has become a hot issue; As a result, distributed electricity supply in the micro grid is the basis for the achievement of the vital objectives of successfully providing the customer with currency and stability. The article proposes a hybrid metaheuristic approach based on the Eagle strategy Technique (ES) and Particular Swarm Optimizing (PSO) Technology, which will minimize low-voltage running costs from a renewable energy source such as an electricity generator, solar panels, wind generators, micro turbines and fuel cells. The cost optimization problem is set up as a nonlinearly constrained problem. In order to maximize distributed generation, a mathematical problem must be solved. The proposed hybrid solution is evaluated on low-voltage micro grids, and its optimal performance is compared to that of other hybrid approaches and variety of other metaheuristic techniques


Energy ◽  
2018 ◽  
Vol 148 ◽  
pp. 1116-1139 ◽  
Author(s):  
Sharmistha Sharma ◽  
Subhadeep Bhattacharjee ◽  
Aniruddha Bhattacharya

2020 ◽  
pp. 147592172097970
Author(s):  
Liangliang Cheng ◽  
Vahid Yaghoubi ◽  
Wim Van Paepegem ◽  
Mathias Kersemans

The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.


2021 ◽  
Vol 11 (3) ◽  
pp. 1325
Author(s):  
Dalia Yousri ◽  
Magdy B. Eteiba ◽  
Ahmed F. Zobaa ◽  
Dalia Allam

In this paper, novel variants for the Ensemble Particle Swarm Optimizer (EPSO) are proposed where ten chaos maps are merged to enhance the EPSO’s performance by adaptively tuning its main parameters. The proposed Chaotic Ensemble Particle Swarm Optimizer variants (C.EPSO) are examined with complex nonlinear systems concerning equal order and variable-order fractional models of Permanent Magnet Synchronous Motor (PMSM). The proposed variants’ results are compared to that of its original version to recommend the most suitable variant for this non-linear optimization problem. A comparison between the introduced variants and the previously published algorithms proves the developed technique’s efficiency for further validation. The results emerge that the Chaotic Ensemble Particle Swarm variants with the Gauss/mouse map is the most proper variant for estimating the parameters of equal order and variable-order fractional PMSM models, as it achieves better accuracy, higher consistency, and faster convergence speed, it may lead to controlling the motor’s unwanted chaotic performance and protect it from ravage.


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