scholarly journals Optimal Coordination of DOC Relays Incorporated into a Distributed Generation-Based Micro-grid Using a Meta-Heuristic MVO Algorithm

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4115 ◽  
Author(s):  
Abdelsalam ◽  
Diab
Keyword(s):  
Distributed Generation ◽  
Optimization Problem ◽  
Operating Time ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Optimal Coordination ◽  
Micro Grid ◽  
Bus Networks ◽  
Micro Grids ◽  
Better Than

Distributed, generation-based micro-grids are increasingly being used in the build-up of the modern power system. However, the protection of these micro-grids has many challenges. One of the important challenges is the coordination of directional overcurrent (DOC) relays. The optimization of the coordination of DOC relays is considered a nonlinear programming problem with pre-defined constrains. In this paper, the problem of the optimal coordination of DOC relays is solved using a multi-verse optimization (MVO) algorithm which is inspired from cosmology science. The proposed algorithm is tested by applying it to Institute of Electrical and Electronics Engineers (IEEE) 3 bus and IEEE 9 bus networks. The performance of the proposed algorithm is compared with the particle swarm optimization (PSO) algorithm when applied to both networks. All results show that the performance of the MVO algorithm is better than PSO in terms of its reduction of both the overall operating time (OT) of DOC relays and the computational burden of the computer solving the optimization problem.

scholarly journals A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
An Liu ◽  
Ming-Ta Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Operating Time ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Method ◽  
Swarm Optimization ◽  
Simplex Search ◽  
Simplex Search Method ◽  
Overcurrent Relays

Coordination optimization of directional overcurrent relays (DOCRs) is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS) and pickup current (Ip) values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO) algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO) algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.

The Calculation of Photovoltaic System Generation Access Capacity in Distribution Network Based on Probabilistic Power Flow and the PSO Optimization

2012 ◽  
Vol 529 ◽  
pp. 371-375
Author(s):  
Lu Yao Ma ◽  
Shu Jun Yao ◽  
Yan Wang ◽  
Jing Yang ◽  
Long Hui Liu
Keyword(s):  
Distributed Generation ◽  
Power Flow ◽  
Optimization Problem ◽  
Flow Model ◽  
Expansion Method ◽  
Pso Algorithm ◽  
Photovoltaic System ◽  
System Generation ◽  
Probabilistic Power Flow ◽  
The Cost

With the distributed generation such as photovoltaic power system (PVS) is largely introduced into power grid, some significant problems such as system instability problem increase seriously. In order to make full use of PVS and make sure the voltage exceeding probability is limited within a certain range to ensure the power quality, as well as consider the cost of access device, the suitable PVS access node and capacity is important. Based on this problem, this paper establishes the probabilistic power flow model of PVS by introducing the combined Cumulants and the Gram-Charlier expansion method. Also, to solve the nonlinear combinatorial optimization problem, this paper uses PSO algorithm. Finally to get the suitable PVS access node and capacity, also calculate the solution of voltage exceeding probability.

ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM

2009 ◽  
Vol 05 (02) ◽  
pp. 487-496 ◽  
Author(s):  
WEI FANG ◽  
JUN SUN ◽  
WENBO XU
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Novel Mutation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mutation Operator ◽  
Fundamental Theory ◽  
Swarm Optimization ◽  
Mutation Operators ◽  
Better Than

Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

Convergence Analysis of Particle Swarm Optimization via Illustration Styles

2021 ◽  
pp. 29-37
Author(s):  
Wei-Der Chang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Analysis ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Population Based Algorithm

Particle swarm optimization (PSO) is the most important and popular algorithm to solving the engineering optimization problem due to its simple updating formulas and excellent searching capacity. This algorithm is one of evolutionary computations and is also a population-based algorithm. Traditionally, to demonstrate the convergence analysis of the PSO algorithm or its related variations, simulation results in a numerical presentation are often given. This way may be unclear or unsuitable for some particular cases. Hence, this paper will adopt the illustration styles instead of numeric simulation results to more clearly clarify the convergence behavior of the algorithm. In addition, it is well known that three parameters used in the algorithm, i.e., the inertia weight w, position constants c1 and c2, sufficiently dominate the whole searching performance. The influence of these parameter settings on the algorithm convergence will be considered and examined via a simple two-dimensional function optimization problem. All simulation results are displayed using a series of illustrations with respect to various iteration numbers. Finally, some simple rules on how to suitably assign these parameters are also suggested

Optimal Coordination of DOCR for Radial Distribution Systems in Presence of TCSC

2016 ◽  
Vol 7 (2) ◽  
pp. 311
Author(s):  
Lazhar Bougouffa ◽  
Abdelaziz Chaghi
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Operating Time ◽  
Pso Algorithm ◽  
Independent Variables ◽  
Dial Setting ◽  
Optimal Coordination ◽  
Protective Relays ◽  
The Impact ◽  
Two Independent Variables

<p>Protective relays coordination is the process of determining the exact relay settings such that the relay closes to the fault would operates faster than other relays. The operating time of each relay depends on two independent variables called Pickup current (Ip) and Time Dial Setting (TDS). In this paper, a PSO algorithm has been presented to determine the coordination of Directional Over-Current Relays (DOCRs) in presence of multi-system FACTS devises. From the simulation result and analysis, the impact of TCSC location in the in 33-bus distribution system on Directional Over-Current Relays has been observed on the optimal relays settings as well as the effectiveness of the proposed algorithm in finding optimal coordination of directional over-current relays.</p>

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

The Motion Planning for Spherical Robot with Two Motors

2015 ◽  
Vol 789-790 ◽  
pp. 688-692
Author(s):  
Xin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Angular Velocity ◽  
Motion Planning ◽  
Coordinate System ◽  
Optimization Problem ◽  
Kinematic Model ◽  
Pso Algorithm ◽  
Spherical Robot ◽  
Swarm Optimization ◽  
Moving Coordinate

In this paper, we proposed a spherical robot with two motors in the horizontal and vertical directions which derive the robot to do omni-directionally roll. Based on the structure of the robot, we derived the kinematic model using inertial and moving coordinate system. In order to minimize the energy of the system, an optimization problem with two optimization variables which are the parameters to control the angular velocity of the motors is given. After that, a particle swarm optimization (PSO) algorithm is used to solve the optimization problem. The simulation shows that the motion planning with the algorithm has high precision.

A New Simple Micro-PSO for High Dimensional Optimization Problem

2012 ◽  
Vol 236-237 ◽  
pp. 1195-1200
Author(s):  
Wen Hua Han
Keyword(s):  
Optimization Problem ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Population Based ◽  
Global Optimum ◽  
Stochastic Search ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Search Optimization ◽  
Dimensional Optimization

The particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search optimization technique, which has already been widely used to various of fields. In this paper, a simple micro-PSO is proposed for high dimensional optimization problem, which is resulted from being introduced escape boundary and perturbation for global optimum. The advantages of the simple micro-PSO are more simple and easily implemented than the previous micro-PSO. Experiments were conducted using Griewank, Rosenbrock, Ackley, Tablets functions. The experimental results demonstrate that the simple micro-PSO are higher optimization precision and faster convergence rate than PSO and robust for the dimension of the optimization problem.

scholarly journals Beetle swarm optimization algorithm: Theory and application

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5121-5137
Author(s):  
Tiantian Wang ◽  
Long Yang ◽  
Qiang Liu
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Pso Algorithm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Algorithm Theory ◽  
Grasshopper Optimization ◽  
Practical Impact

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization (BSO) algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and grasshopper optimization algorithm (GOA). Numerical experiments show that the BSO algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau?s optimization problem, are also considered and the proposed BSO algorithm is shown to be competitive in those applications.

Dexterity Analysis based on Jacobian and Performance Optimization for Multi-segment Continuum Robots

2021 ◽  
pp. 1-13
Author(s):  
Jiao Wang ◽  
Henry Y K Lau
Keyword(s):  
Performance Optimization ◽  
Optimization Problem ◽  
Jacobian Matrix ◽  
Pso Algorithm ◽  
Optimal Length ◽  
Continuum Robots ◽  
Swarm Optimization ◽  
Global Indices ◽  
Reachable Workspace ◽  
And Performance

Abstract This study presents the performance analysis of multi- segment continuum robots. Since continuum robots are designed to provide excellent dexterity, two local indices, axiality and angularity dexterity, are introduced to study the dexterity that is inspired by separating Jacobian matrix. A Monte Carlo Method is adopted to simulate the distribution of local dexterity over the workspace. On this basis, the corresponding global indices in axiality and angularity are defined to compare global dexterity performance. To investigate the optimal kinematic performance, an objective function related to the segment lengths is designed under the consideration of reachable workspace as well as dexterity performance. Particle Swarm Optimization (PSO) algorithm is adopted to solve the optimization problem successfully. The optimal length distributions for two-segment and three-segment continuum robots are discovered. It is found that this method can also apply to general multi-segment continuum robots.

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