Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems

2018 ◽  
Vol 31 (11) ◽  
pp. 7707-7732 ◽  
Author(s):  
Tri Phuoc Nguyen ◽  
Tung The Tran ◽  
Dieu Ngoc Vo
Keyword(s):  
Distribution Systems ◽  
Search Algorithm ◽  
Distributed Generators ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

scholarly journals Optimal Reactive Power Generation for Radial Distribution Systems Using a Highly Effective Proposed Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-36
Author(s):  
Le Chi Kien ◽  
Thuan Thanh Nguyen ◽  
Bach Hoang Dinh ◽  
Thang Trung Nguyen
Keyword(s):  
Power Loss ◽  
Distribution Systems ◽  
Reactive Power ◽  
Search Algorithm ◽  
Diffusion Mechanism ◽  
Total Power ◽  
Total Cost ◽  
The Third ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

In this paper, a proposed modified stochastic fractal search algorithm (MSFS) is applied to find the most appropriate site and size of capacitor banks for distribution systems with 33, 69, and 85 buses. Two single-objective functions are considered to be reduction of power loss and reduction of total cost of energy loss and capacitor investment while satisfying limit of capacitors, limit of conductor, and power balance of the systems. MSFS was developed by performing three new mechanisms including new diffusion mechanism and two new update mechanisms on the conventional stochastic fractal search algorithm (SFS). As a result, MSFS can reduce 0.002%, 0.003%, and 0.18% of the total power loss from SFS for the three study systems. As compared to other methods, MSFS can reduce power loss from 0.07% to 3.98% for the first system, from 3.7% to 7.3% for the second system, and from 0.92% to 6.98% for the third system. For the reduction of total cost, the improvement level of the proposed method over SFS and two other methods is more significant. It is 0.03%, 1.22%, and 5.76% for the second system and 2.31%, 0.87%, and 3.77% for the third system. It is emphasized that the proposed method can find the global optimal solutions for all study cases while SFS was still implementing search process nearby or far away from the solutions. Furthermore, MSFS can converge to the best solutions much faster than these compared methods. Consequently, it can be concluded that the proposed method is very effective for finding the best location and size of added capacitors in distribution power systems.

scholarly journals Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Tung Tran The ◽  
Dieu Vo Ngoc ◽  
Nguyen Tran Anh
Keyword(s):  
Diffusion Process ◽  
Power Loss ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Power Loss Reduction ◽  
Voltage Profile Improvement ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

This paper proposes a chaotic stochastic fractal search algorithm (CSFSA) method to solve the reconfiguration problem for minimizing the power loss and improving the voltage profile in distribution systems. The proposed method is a metaheuristic method developed for overcoming the weaknesses of the conventional SFSA with two processes of diffuse and update. In the first process, new points will be created from the initial points by the Gaussian walk. For the second one, SFSA will update better positions for the particles obtained in the diffusion process. In addition, this study has also integrated the chaos theory to improve the SFSA diffusion process as well as increase the rate of convergence and the ability to find the optimal solution. The effectiveness of the proposed CSFSA has been verified on the 33-bus, 84-bus, 119-bus, and 136-bus distribution systems. The obtained results from the test cases by CSFSA have been verified to those from other natural methods in the literature. The result comparison has indicated that the proposed method is more effective than many other methods for the test systems in terms of power loss reduction and voltage profile improvement. Therefore, the proposed CSFSA can be a very promising potential method for solving the reconfiguration problem in distribution systems.

scholarly journals Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Fran Sérgio Lobato ◽  
Gustavo Barbosa Libotte ◽  
Gustavo Mendes Platt
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Real Data ◽  
The Novel ◽  
Estimation Process ◽  
Design Variables ◽  
Novel Coronavirus ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.

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