scholarly journals Investigation on Photovoltaic Array Modeling and the MPPT Control Method under Partial Shading Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jianbo Bai ◽  
Leihou Sun ◽  
Rupendra Kumar Pachauri ◽  
Guangqing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Output Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Control Mode ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Pv Array ◽  
Pv Modules

On the basis of a five-parameter photovoltaic (PV) mathematical model, a multipeak output model of a PV array under partial shading conditions (PSCs) is obtained by MATLAB simulation. Simulation and experimental results demonstrate that the model can simulate the performance curves of the PV array under the PSCs. Optimized particle swarm optimization (OPSO) is used to control the multipeak output model that can quickly and accurately track the global maximum power point (GMPP) of PV modules under PSCs. Its main idea is to determine the initial position of particles and remove the acceleration factor and random number in traditional particle swarm optimization (PSO) algorithm. Additionally, according to the distance between two consecutive peak points, the maximum value of velocity is obtained. The advantages of the OPSO include the following: compared with the traditional PSO algorithm, the computing time is greatly shortened; and it is easy to achieve the MPPT with a low-cost microprocessor. In addition, a PV optimizer is designed to improve the output power of PV modules under PSCs, and simulation and experimentation have compared the output characteristics of PV modules in traditional control mode and optimized control mode under PSCs. The experimental results show that the PV optimizer improves the output power of the PV modules by 13.4% under the PSC.

Multi-Objective Particle Swarm Optimization with Comparison Scheme and New Pareto-Optimal Search Strategy

2014 ◽  
Vol 496-500 ◽  
pp. 1895-1900
Author(s):  
Wen Wang ◽  
Wei Shen ◽  
Chao Long Ying ◽  
Xin Yi Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Search Strategy ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Optimal Search ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Convergence Performance

In the presented article, a novel multi-objective PSO algorithm, RP-MOPSO has been proposed. The algorithm adopts a new comparison scheme for position upgrading. The scheme is simple but effective in improve algorithms convergence speed. A sigma-density strategy of selecting the global best particle for each particle in swarm based on a new solutions density definition is designed. Experimental results on seven functions show that proposed algorithm show better convergence performance than other classical MOP algorithms. Meanwhile the proposed algorithm is more effective in maintaining the diversity of the solutions.

Particle Swarm Optimization Based Solar PV Array Reconfiguration of the Maximum Power Extraction Under Partial Shading Conditions

2018 ◽  
Vol 9 (1) ◽  
pp. 74-85 ◽  
Author(s):  
Thanikanti Sudhakar Babu ◽  
J. Prasanth Ram ◽  
Tomislav Dragicevic ◽  
Masafumi Miyatake ◽  
Frede Blaabjerg ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Maximum Power ◽  
Solar Pv ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Extraction ◽  
Pv Array

scholarly journals Using Particle Swarm Optimization to Accurately Identify Syntactic Phrases in Free Text

2018 ◽  
Vol 8 (1) ◽  
pp. 63-77 ◽  
Author(s):  
George Tambouratzis
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Training Data ◽  
Target Language ◽  
Free Text ◽  
Swarm Optimization ◽  
Training Sequences ◽  
Repulsive Forces

Abstract The present article reviews the application of Particle Swarm Optimization (PSO) algorithms to optimize a phrasing model, which splits any text into linguistically-motivated phrases. In terms of its functionality, this phrasing model is equivalent to a shallow parser. The phrasing model combines attractive and repulsive forces between neighbouring words in a sentence to determine which segmentation points are required. The extrapolation of phrases in the specific application is aimed towards the automatic translation of unconstrained text from a source language to a target language via a phrase-based system, and thus the phrasing needs to be accurate and consistent to the training data. Experimental results indicate that PSO is effective in optimising the weights of the proposed parser system, using two different variants, namely sPSO and AdPSO. These variants result in statistically significant improvements over earlier phrasing results. An analysis of the experimental results leads to a proposed modification in the PSO algorithm, to prevent the swarm from stagnation, by improving the handling of the velocity component of particles. This modification results in more effective training sequences where the search for new solutions is extended in comparison to the basic PSO algorithm. As a consequence, further improvements are achieved in the accuracy of the phrasing module.

Route Planning of Geomagnetic Aided Navigation for Vehicle under Matching Suitability Constraints

2013 ◽  
Vol 454 ◽  
pp. 39-42
Author(s):  
Peng Wang ◽  
Xiao Ping Hu ◽  
Mei Ping Wu ◽  
Hua Mu ◽  
Hai Ping Yuan
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Route Planning ◽  
Pso Algorithm ◽  
Planning Method ◽  
Experimental Results ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Movement Distance

In geomagnetic aided navigation (GAN), the vehicle is expected to traverse the areas with excellent matching suitability in order to obtain high matching precision. The route planning problem under matching suitability constraints is studied based on particle swarm optimization (PSO) algorithm in this article. Firstly, the PSO algorithm is briefly introduced and the expanding space of route nodes is determined with the maneuverability constraints of the vehicle. Then the minimum movement distance, the ability of avoiding threats and the proximity to suitable-matching areas are considered to construct the fitness function of PSO algorithm. Further the route planning method under matching suitability constraints is proposed. Experimental results show that the proposed method is effective, and the vehicle can successfully avoid the threats and can traverse the suitable-matching areas.

scholarly journals Photovoltaic maximum power point tracking under dynamic partial shading changes by novel adaptive particle swarm optimization strategy

2019 ◽  
Vol 42 (1) ◽  
pp. 104-115 ◽  
Author(s):  
Ali M Eltamaly ◽  
Mamdooh S Al-Saud ◽  
Ahmed G Abokhalil ◽  
Hassan MH Farh
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Change ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Pv Array ◽  
Power Point

Maximum power point tracker (MPPT) is vital device in the Photovoltaic (PV) system because it can increase the generated power considerably. Partial shading conditions (PSCs) on the PV array generates many peaks in the P-V curve of PV array. Metaheuristic techniques like particle swarm optimization (PSO) have the ability to track the global peak (GP) at any operating conditions. PSO technique can track the GP but once the shading pattern (SP) changes, the value and location of the new GP will change and may PSO cannot catch the new GP because all particles are busy around the previous GP. This problem is classified into two conditions: the first condition if the GP change its location and value suddenly, the second condition occurs when the GP changes its value gradually and still in same place. The first problem is solved by reinitializing the particles. The second problem is solved using a new adaptive strategy that has not been treated or adopted in any literature before. The results obtained prove the superiority of the new proposed strategy in always catching GP in dynamic change PSCs.

An Inverse Research Method for the Radiation Characteristics Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 860-863 ◽  
pp. 867-871
Author(s):  
Ming Yan Wu ◽  
Qun Zhi Zhu
Keyword(s):  
Refractive Index ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Calculation Model ◽  
Absorption Index ◽  
Experimental Results ◽  
Radiation Characteristics ◽  
Swarm Optimization ◽  
Calculation Results

Based on the experimental results of transmittance, particle swarm optimization (PSO) algorithm was adopted to establish inverse research model to calculate the refractive index and absorption index of nanofluids, we used the inverse calculation model to calculate the refractive index and absorption index of water and aqueous nanofluids, comparing inverse calculation results with experimental results, it turned out that inverse calculation model can accurately calculate the refractive index and absorption index of nanofluids.

Particle Swarm Optimization MPPT Method for PV Materials in Partial Shading

2011 ◽  
Vol 321 ◽  
pp. 72-75 ◽  
Author(s):  
Chun Xia Liu ◽  
Li Qun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Particle Swarm ◽  
Response Speed ◽  
Pso Algorithm ◽  
Development Stage ◽  
Partial Shading ◽  
Swarm Optimization ◽  
The Real ◽  
Point Tracking

Today, the large-scale Photovoltaic (PV) power system, connected to grid, is in their advanced development stage and is extremely interest in whole world. The real large-scale PV array can be partially shaded by the shadow of building, cloud, bird and dirt. The output characteristic of PV materials in partially shaded conditions is strong nonlinear, and there are multi local peaks in output power voltage curve, and the only one real peak exists in these local peaks. Certainly, the maximum power point tracking (MPPT) method is very important to extract the as much as possible energy from the costly PV materials. The variant weight Particle Swarm Optimization (PSO) method is proposed to track the real peak by using the excellent multi-peak value optimization characteristic of PSO algorithm. The simulation results shows that the proposed PSO method can improve the response speed and output efficiency of PV materials in partial shading as compared to the conventional Incremental conductance (IC) method.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

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