Maximum Power Control for Photovoltaic System Using Two Strategies

2014 ◽  
pp. 131-142
Author(s):  
N. Harrabi ◽  
E. Kamal ◽  
A. Aitouche ◽  
M. Souissi
Keyword(s):  
Power Control ◽  
Maximum Power ◽  
Photovoltaic System

Power Quality Improvement for Grid-Connected Photovoltaic Panels Using Direct Power Control

2022 ◽  
pp. 107-142
Author(s):  
Arezki Fekik ◽  
Mohamed Lamine Hamida ◽  
Hamza Houassine ◽  
Ahmad Taher Azar ◽  
Nashwa Ahmad Kamal ◽  
...  
Keyword(s):  
Power Control ◽  
Power Quality ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Direct Power ◽  
Direct Power Control ◽  
Power Quality Improvement ◽  
Two Phases ◽  
Bus Voltage ◽  
Power Point

This chapter displays a control strategy for a photovoltaic system (PV) linked to the network with two phases of a PWM converter, where the first phase is a DC-DC converter linked among the photovoltaic source and the DC-AC converter. The second phase is a DC-AC converter linked to the grid. The maximum power point (MPP) is tracked by DC-DC converter, which increases the DC bus voltage. The P&O (perturbation and observation) technique is utilized as a direct current (DC-DC) converter controller to make the PV arrays work at greatest value of power under changing weather conditions. The DC-AC converter transfers the maximum power extracted from the PV cell into the grid. To improve the energy quality produced by the photovoltaic field other than the performance of the pulse width modulation (PWM) inverter, direct power control (DPC) is used to achieve these improvements. The simulation results showed a good performance of the suggested controller. Decoupled power control is achieved successfully, and a good power quality with low harmonic distortion rate (THD) is obtained.

Optimal Command for Photovoltaic Systems in Real Outdoor Weather Conditions

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Hafsa Abouadane ◽  
Abderrahim Fakkar ◽  
Benyounes Oukarfi
Keyword(s):  
Maximum Power Point Tracking ◽  
Accurate Determination ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

The photovoltaic panel is characterized by a unique point called the maximum power point (MPP) where the panel produces its maximum power. However, this point is highly influenced by the weather conditions and the fluctuation of load which drop the efficiency of the photovoltaic system. Therefore, the insertion of the maximum power point tracking (MPPT) is compulsory to track the maximum power of the panel. The approach adopted in this paper is based on combining the strengths of two maximum power point tracking techniques. As a result, an efficient maximum power point tracking method is obtained. It leads to an accurate determination of the MPP during different situations of climatic conditions and load. To validate the effectiveness of the proposed MPPT method, it has been simulated in matlab/simulink under different conditions.

scholarly journals A Novel DSP-Based MPPT Control Design for Photovoltaic Systems Using Neural Network Compensator

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3260
Author(s):  
Ming-Fa Tsai ◽  
Chung-Shi Tseng ◽  
Kuo-Tung Hung ◽  
Shih-Hua Lin
Keyword(s):  
Neural Network ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Solar Irradiation ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Irradiation Cell ◽  
Power Point ◽  
Neural Network Compensator

In this study, based on the slope of power versus voltage, a novel maximum-power-point tracking algorithm using a neural network compensator was proposed and implemented on a TI TMS320F28335 digital signal processing chip, which can easily process the input signals conversion and the complex floating-point computation on the neural network of the proposed control scheme. Because the output power of the photovoltaic system is a function of the solar irradiation, cell temperature, and characteristics of the photovoltaic array, the analytic solution for obtaining the maximum power is difficult to obtain due to its complexity, nonlinearity, and uncertainties of parameters. The innovation of this work is to obtain the maximum power of the photovoltaic system using a neural network with the idea of transferring the maximum-power-point tracking problem into a proportional-integral current control problem despite the variation in solar irradiation, cell temperature, and the electrical load characteristics. The current controller parameters are determined via a genetic algorithm for finding the controller parameters by the minimization of a complicatedly nonlinear performance index function. The experimental result shows the output power of the photovoltaic system, which consists of the series connection of two 155-W TYN-155S5 modules, is 267.42 W at certain solar irradiation and ambient temperature. From the simulation and experimental results, the validity of the proposed controller was verified.

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