A MPPT Control Method of PV System Based on Fuzzy Logic and Particle Swarm Optimization

2012 ◽  
Author(s):  
Yufeng Hu ◽  
Jun Liu ◽  
Bin Liu
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control Method ◽  
Particle Swarm ◽  
Pv System ◽  
Swarm Optimization

scholarly journals Improved PSO: A Comparative Study in MPPT Algorithm for PV System Control under Partial Shading Conditions

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2035
Author(s):  
Wafa Hayder ◽  
Emanuele Ogliari ◽  
Alberto Dolara ◽  
Aycha Abid ◽  
Mouna Ben Hamed ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Control Method ◽  
Particle Swarm ◽  
Climatic Conditions ◽  
System Control ◽  
Pv System ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Ipso Algorithm

This paper deals with the implementation and analysis of a new maximum power point tracking (MPPT) control method, which is tested under variable climatic conditions. This new MPPT strategy has been created for photovoltaic systems based on Particle Swarm Optimization (PSO). The novel Improved Particle Swarm Optimization (IPSO) algorithm is tested in several simulations which have been implemented in view of the various system responses such as: voltage, current, and power. The performances of the proposed IPSO algorithm have been completed and compared with results of well-established methods adopted in the literature showing a higher accuracy.

A Novel Combined Particle Swarm Optimization and Genetic Algorithm MPPT Control Method for Multiple Photovoltaic Arrays at Partial Shading

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Liqun Liu ◽  
Chunxia Liu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Large Scale ◽  
Control Method ◽  
Particle Swarm ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Point

The output characteristics of multiple photovoltaic (PV) arrays at partial shading are characterized by multiple steps and peaks. This makes that the maximum power point tracking (MPPT) of a large scale PV system becomes a difficult task. The conventional MPPT control method was unable to track the maximum power point (MPP) under random partial shading conditions, making the output efficiency of the PV system is low. To overcome this difficulty, in this paper, an improved MPPT control method with better performance based on the genetic algorithm (GA) and adaptive particle swarm optimization (APSO) algorithm is proposed to solve the random partial shading problem. The proposed genetic algorithm adaptive particle swarm optimization (GAAPSO) method conveniently can be used in the real-time MPPT control strategy for large scale PV system, and the implementation of the collect circuit is easy to gain the global peak of multiple PV arrays, thereby resulting in lower cost, higher overall efficiency. The proposed GAAPSO method has been experimentally validated by using several illustrative examples. Simulations and experimental results demonstrate that the GAAPSO method provides effective, fast, and perfect tracking.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

scholarly journals Proportional-Integral Controllers Performance of a Grid-Connected Solar PV System with Particle Swarm Optimization and Ziegler–Nichols Tuning Method

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2516
Author(s):  
Klemen Deželak ◽  
Peter Bracinik ◽  
Klemen Sredenšek ◽  
Sebastijan Seme
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution Network ◽  
Particle Swarm ◽  
Open Loop ◽  
Limited Area ◽  
Solar Pv ◽  
Pv System ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Tuning Method

This paper deals with photovoltaic (PV) power plant modeling and its integration into the medium-voltage distribution network. Apart from solar cells, a simulation model includes a boost converter, voltage-oriented controller and LCL filter. The main emphasis is given to the comparison of two optimization methods—particle swarm optimization (PSO) and the Ziegler–Nichols (ZN) tuning method, approaches that are used for the parameters of Proportional-Integral (PI) controllers determination. A PI controller is commonly used because of its performance, but it is limited in its effectiveness if there is a change in the parameters of the system. In our case, the aforementioned change is caused by switching the feeders of the distribution network from an open-loop to a closed-loop arrangement. The simulation results have claimed the superiority of the PSO algorithm, while the classical ZN tuning method is acceptable in a limited area of operation.

scholarly journals Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

2008 ◽  
Vol 5 (2) ◽  
pp. 247-262 ◽  
Author(s):  
Boumediene Allaoua ◽  
Abderrahmani Abdessalam ◽  
Gasbaoui Brahim ◽  
Nasri Abdelfatah
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Speed Control ◽  
Dc Motor ◽  
Motor Speed ◽  
Swarm Optimization

scholarly journals Fuzzy and Position Particle Swarm Optimized Routing in VANET

2021 ◽  
Vol 12 (4) ◽  
pp. 199-206
Author(s):  
Viswanathan Ramasamy ◽  
Jagatheswari Srirangan ◽  
Praveen Ramalingam
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Routing Algorithms ◽  
Transport Systems ◽  
Swarm Optimization ◽  
Packet Delivery ◽  
End To End Delay ◽  
End To End ◽  
Fuzzy Logic Technique

In Intelligent Transport Systems, traffic management and providing stable routing paths between vehicles using vehicular ad hoc networks (VANET's) is critical. Lots of research and several routing techniques providing a long path lifetime have been presented to resolve this issue. However, the routing algorithms suffer excessive overhead or collisions when solving complex optimization problems. In order to improve the routing efficiency and performance in the existing schemes, a Position Particle Swarm Optimization based on Fuzzy Logic (PPSO-FL) method is presented for VANET that provides a high-quality path for communication between nodes. The PPSO-FL has two main steps. The first step is selecting candidate nodes through collectively coordinated metrics using the fuzzy logic technique, improving packet delivery fraction, and minimizing end-to-end delay. The second step is the construction of an optimized routing model. The optimized routing model establishes an optimal route through the candidate nodes using position-based particle swarm optimization. The proposed work is simulated using an NS2 simulator. Simulation results demonstrate that the method outperforms the standard routing algorithms in packet delivery fraction, end-to-end delay and execution time for routing in VANET scenarios.

scholarly journals A New Optimal Fuzzy Logic Guidance Law using Time Variant Particle Swarm Optimization

2013 ◽  
Vol 72 (7) ◽  
pp. 34-37
Author(s):  
Labeed Hassan ◽  
Seyed Hossein Sadati ◽  
Mohamad Bagher Malaeak ◽  
Mohamad Ali Ashtiani ◽  
Jalal Karimi
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Guidance Law
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