scholarly journals Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1913
Author(s):  
Luis Fernando Grisales-Noreña ◽  
Oscar Danilo Montoya ◽  
Ricardo Alberto Hincapié-Isaza ◽  
Mauricio Granada Echeverri ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Location Problem ◽  
Search Algorithm ◽  
Optimization Method ◽  
Population Based ◽  
Power Losses ◽  
Test Systems ◽  
Processing Times ◽  
Distributed Generators ◽  
Parallel Version

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed.

scholarly journals Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2837
Author(s):  
Andrés Alfonso Rosales Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Distributed Generation ◽  
Direct Current ◽  
Mathematical Formulation ◽  
Average Power ◽  
Power Losses ◽  
Salp Swarm Algorithm ◽  
Test Systems ◽  
Distributed Generators ◽  
Optimal Power ◽  
Solution Methodology

This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size.

Parallel Scatter Search Approach for the MinMax Regret Location Problem

2018 ◽  
Vol 9 (2) ◽  
pp. 1-17
Author(s):  
Sarah Ibri ◽  
Mohammed EL Amin Cherabrab ◽  
Nasreddine Abdoune
Keyword(s):  
Location Problem ◽  
Search Algorithm ◽  
Discrete Event ◽  
Scatter Search ◽  
Search Time ◽  
Computation Time ◽  
Minmax Regret ◽  
Search Approach ◽  
Two Stages

In this paper we propose an efficient solving method based on a parallel scatter search algorithm that accelerates the search time to solve the minmax regret location problem. The algorithm was applied in the context of emergency management to locate emergency vehicles stations. A discrete event simulator was used to test the quality of the obtained solutions on the operational level. We compared the performance of the algorithm to an existing two stages method, and experiments show the efficiency of the proposed method in terms of quality of solution as well as the gain in computation time that could be obtained by parallelizing the proposed algorithm.

scholarly journals Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Xu Chen ◽  
Bin Xu ◽  
Kunjie Yu ◽  
Wenli Du
Keyword(s):  
Real World ◽  
Teaching And Learning ◽  
Chemical Engineering ◽  
Search Algorithm ◽  
Population Based ◽  
Metaheuristic Search ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Real World Problems

Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

scholarly journals Optimal Location and Sizing of Distributed Generators in DC Networks Using a Hybrid Method Based on Parallel PBIL and PSO

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1808
Author(s):  
Luis Fernando Grisales-Noreña ◽  
Oscar Danilo Montoya ◽  
Carlos Andrés Ramos-Paja ◽  
Quetzalcoatl Hernandez-Escobedo ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Hybrid Methods ◽  
Population Based ◽  
Optimal Location ◽  
Swarm Optimization ◽  
Distributed Generators ◽  
Parallel Version ◽  
Location Methods ◽  
Hybrid Methodology

This paper addresses the problem of the locating and sizing of distributed generators (DGs) in direct current (DC) grids and proposes a hybrid methodology based on a parallel version of the Population-Based Incremental Learning (PPBIL) algorithm and the Particle Swarm Optimization (PSO) method. The objective function of the method is based on the reduction of the power loss by using a master-slave structure and the consideration of the set of restrictions associated with DC grids in a distributed generation environment. In such a structure, the master stage (PPBIL) finds the location of the generators and the slave stage (PSO) finds the corresponding sizes. For the purpose of comparison, eight additional hybrid methods were formed by using two additional location methods and two additional sizing methods, and this helped in the evaluation of the effectiveness of the proposed solution. Such an evaluation is illustrated with the electrical test systems composed of 10, 21 and 69 buses and simulated on the software, MATLAB. Finally, the results of the simulation demonstrated that the PPBIL–PSO method obtains the best balance between the reduction of power loss and the processing time.

scholarly journals Optimization of location and size of distributed generations for maximizing their capacity and minimizing power loss of distribution system based on cuckoo search algorithm

2021 ◽  
Vol 10 (4) ◽  
pp. 1769-1776
Author(s):  
Thuan Thanh Nguyen ◽  
Trieu Ton Ngoc ◽  
Thang Trung Nguyen ◽  
Thanh-Phuc Nguyen ◽  
Ngoc Au Nguyen
Keyword(s):  
Distribution System ◽  
Power Loss ◽  
Search Algorithm ◽  
Distribution Network ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Power Losses ◽  
Distributed Generations ◽  
Test Systems ◽  
Reliable Methods

Maximizing capacity of distributed generations (DGs) embed into distribution network is a solution to attract investment for DGs installation on the distribution system. This paper introduces a approach of optimizing location and capacity of DGs for maximizing DGs capacity and minimizing the system’s power loss based on cuckoo search algorithm (CSA). The proposed problem and method are simulated on two test systems including the 33-node and 69-node networks. The numerical results have demonstrated that the proposed method not only reduces power losses but also maximizes the power of DGs embed into the distribution network. The results also introduce that the proposed CSA method is better performance that some previous methods in terms of power loss and DGs capacity. The results obtained in many independent runs for two test systems indicate that CSA in one of the reliable methods for the DGs placement problems.

scholarly journals Improve the Voltage Profile by using DGS and Dstatcom Optimum Placement and Sizing with Help of Back Tracking Search Algorithm

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1255-1260
Keyword(s):  
Power Systems ◽  
Distributed System ◽  
Energy Demand ◽  
Search Algorithm ◽  
Electrical Energy ◽  
Voltage Profile ◽  
Power Losses ◽  
Distributed Generators ◽  
Optimum Placement ◽  
The Ideal

In Modern Trends in power system, the Distributed Generators (DGs) and FACT Devices are used in Distributed System are becoming more important due to the increase the electrical energy demand. The sizing off DGs and DSTATCOM sizing and proper location has important in power systems which obtaining their better voltage profiles. In this paper, decide the ideal area and estimating DGs and DSTATCOM to power losses decreases and recover the Voltage Profile by the new swarm advancement method explicitly Back Tracking Search Algorithm (BSA) is considered for various burden models.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Assessing the quality of public health in the Russian Federation and Israel: Main problems and solutions

2020 ◽  
Vol 19 (4) ◽  
pp. 618-632
Author(s):  
A.S. Panchenko
Keyword(s):  
Public Health ◽  
Russian Federation ◽  
Population Based ◽  
The Public ◽  
Disability Adjusted Life ◽  
Problems And Solutions ◽  
The Russian Federation ◽  
Disability Adjusted Life Year ◽  
Practical Recommendations

Subject. The article addresses the public health in the Russian Federation and Israel. Objectives. The focus is on researching the state of public health in Russia and Israel, using the Global Burden of Disease (GBD) project methodology, identifying problem areas and searching for possible ways to improve the quality of health of the Russian population based on the experience of Israel. Methods. The study draws on the ideology of the GBD project, which is based on the Disability-Adjusted Life-Year (DALY) metric. Results. The paper reveals the main causes of DALY losses and important risk factors for cancer for Russia and Israel. The findings show that the total DALY losses for Russia exceed Israeli values. The same is true for cancer diseases. Conclusions. Activities in Israel aimed at improving the quality of public health, the effectiveness of which has been proven, can serve as practical recommendations for Russia. The method of analysis, using the ideology of the GBD project, can be used as a tool for quantitative and comparative assessment of the public health.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

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