scholarly journals Optimized generated power of a solar PV system using an intelligent tracking technique

2021 ◽  
Vol 12 (4) ◽  
pp. 2580
Author(s):  
Afshin Balal ◽  
Mostafa Abedi ◽  
Farzad Shahabi
Keyword(s):  
Output Power ◽  
Solar Pv ◽  
Pv System ◽  
Partial Shading ◽  
Control Approach ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Oscillations ◽  
Solar Pv System ◽  
Power Point

<span lang="EN-US">Partial shading condition (PSC) is common and complicated in all types of PV power plant. Therefore, the power production of solar system would be affected by the mismatch phenomena produced by PSC. Furthermore, when the array is partially shaded, the P–V characteristics become more complex which causes multiple peaks of the P-V curve. So, the simple maximum power point tracking (MPPT) methods such as perturb and observe (P&amp;O) will fail. To address the above issue, this paper proposes a combination of two different approaches, implementing distributed MPPT (DMPPT) and optimized fuzzy/bee algorithm (OFBA). DMPPT can be utilized to maximize solar energy by allowing each module, or group of modules, be managed independently. Also, due to the output power oscillations around the operating point in the P&amp;O method, an OFBA is employed which allowing for the decrease of output power oscillations without the usage of temperature and light sensors. The result shows that utilizing of DMPPT control approach in conjunction with the OFBA boosts the output generated power.</span>

A brief overview of maximum power point tracking algorithm for solar PV system

2021 ◽  
Author(s):  
C. Pavithra ◽  
Pooja Singh ◽  
Venkatesa Prabhu Sundramurthy ◽  
T.S. Karthik ◽  
P.R. Karthikeyan ◽  
...  
Keyword(s):  
Maximum Power Point Tracking ◽  
Tracking Algorithm ◽  
Maximum Power ◽  
Solar Pv ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Solar Pv System ◽  
Power Point

scholarly journals Applying Robust Intelligent Algorithm and Internet of Things to Global Maximum Power Point Tracking of Solar Photovoltaic Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
En-Chih Chang
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Rbf Neural Network ◽  
Global Maximum ◽  
Solar Pv ◽  
Pv System ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Solar Pv System ◽  
Power Point

The important dare in the solar photovoltaic (PV) system is to investigate the performance under partial shading conditions. A robust intelligent algorithm (RIA) connected with internet of things (IoT) is developed to offer the real-time monitoring of solar PV systems, thus ensuring global maximum power point tracking (MPPT). The RIA comprises a limited-time terminal sliding-mode control (LTTSMC) and a quantum particle swarm optimization- (QPSO-) radial basis function (RBF) neural network. The LTTSMC creates a quick limited-system-state convergence time and allows for singularity avoidance. However, if the system ambiguity is overrated or underrated, the tremble phenomenon or steady-state error probably occurs around the LTTSMC. The QPSO-RBF neural network is integrated into LTTSMC to handle plant parameter variations and external load perturbations, thus reducing tremble and steady-state errors. With the aggregation of the RIA and the IoT, the remote monitoring in the solar PV system yields faster convergence to nonsingular points, and it also introduces neural network method to achieve more accurate ambiguity estimation. Experimental results show the mathematical analysis and performance enhancement of a prototype algorithm-controlled solar PV system based on digital signal processing under transient and steady-state loading conditions. Because the proposed solar PV system has notable advantages over the classical terminal-sliding solar PV system in terms of tracking accuracy and robust adaptation, this paper is worthy of reference to designers of relative robust control and neural network learning algorithm.

scholarly journals AI based MPPT Controller for Solar PV System

2019 ◽  
Vol 9 (2S4) ◽  
pp. 592-595
Keyword(s):  
Low Voltage ◽  
Photovoltaic System ◽  
Solar Pv ◽  
Pv System ◽  
Boost Converters ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Logic Control ◽  
Solar Pv System ◽  
Power Point

This article discusses Artificial intelligence based Maximum Power Point Tracking (MPPT) for solar Photo-voltaics based system. MPPT is used to improve the efficiency as well as to raise the output through the photovoltaic system through continuous tracking of MPP. The work demands the use of fuzzy logic control technology; hence the PV-cell is interfaced with a DC step-up converter connected with a dc load. Boost converters convert the output of low voltage DC to output of high voltage DC. Following taken through the solar panel are thermal factor like temperature and isolation. The validation of the proposed controller is also discussed.

Modeling and Simulation of Three Phase Grid Connected Solar PV System

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Pradeep Rai ◽  
Roshan Nayak
Keyword(s):  
Voltage Source ◽  
Solar Pv ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Three Phase ◽  
Solar Pv System ◽  
Power Point ◽  
Control Methodology

This paper proposes a nonlinear control methodology for three phase grid connected of PV generator. It consists of a PV arrays; a voltage source inverter, a grid filter and an electric grid. The controller objectives are threefold: i) ensuring the Maximum power point tracking (MPPT) in the side of PV panels, ii) guaranteeing a power factor unit in the side of the grid, iii) ensuring the global asymptotic stability of the closed loop system. Based on the nonlinear model of the whole system, the controller is carried out using a Lyapunov approach. It is formally shown, using a theoretical stability analysis and simulation results that the proposed controller meets all the objectives.

Sign in / Sign up

Export Citation Format

Share Document