With the increase in reliance on solar energy to produce electricity, so many maximum power point
tracking techniques for photovoltaic panels were developed to maximize the produced energy and a lot of
these are well established in the literature.
These techniques vary in many aspects such as: simplicity,
convergence speed, digital or analogical implementation, required sensors, cost, range of effectiveness, as
well as in other aspects.
This paper presents a comparative study of ten widely-adopted mppt algorithms; their performance is
evaluated from energy point of view using the simulation tool (Matlab), considering different solar
irradiance variations. Also, an economic evaluation has been made to make a comparison according to
performance and cost, to determine the optimal choice.
Fuzzy logic control is used to connect a photovoltaic system to the electrical grid by using three phase fully
controlled converter (inverter), This controller is going to track the maximum power point and inject the
maximum available power from th
e PV system to the grid by determining the trigger angle that must be
applied on the switches: Linguistic variables are going to be chosen to determine the amount of change in
the trigger angle of the inverter to track the maximum power.
Search is based on the first stage DC/DC in the solar photovoltaic system, where it
was appropriate to use Ripple Correlation Control method for tracking the maximum
power point of photovoltaic arrays. The technique takes advantage of the signal ri
pple,
which is automatically present in power converters, where the ripple is interpreted as a
perturbation from which a gradient ascent optimization can be realized. The Basic feature
of Ripple Correlation Control technique converges asymptotically at maximum speed to
the maximum power point, and has simple circuit implementations. And will validate the
results in practice.
This paper shows how to design and implement control circuit in the movement of
pv board to reach to maximal possible output, by designing a system to integrate several
methods of of control with each other. During this work, we will design through
formation
a unified system combine control by light sensors, and control via data base on the other
hand. In addition to compare pv angle in both ways. The proposed circuit designed,
conduct a simulation, and implementation a miniature model simulates reality, and
discussed the result to to conflict the advantage and the goal of using the proposed system.
All that by using micro controller (PIC).
The main goal of this search is to design maximum solar power batteries charging
system, Maximum power point tracking (MPPT) system is used in the photovoltaic (PV)
system consisting of a buck-boost Direct Current DC/DC converter, which is controll
ed by
a microcontroller unit, The microcontroller is programmed with a simple and reliable
MPPT called Incremental Conductance (InCond).
The designed battery charger was tested, and the results obtained had insured about
the permanent control on the battery charging.
Comparison study was done, with PWM solar charger controller, it was obvious by
The experimental results, that the battery get charged in a very short time period
considering of the solar sun light hours per day, and the characteristics of the used solar
panel, which confirm the reliable performance of the suggested charging system.
It is the automatic control engineering knowledge forum, as it
should monitor and control the variables that interact in all
industrial processes to perform functions equipment installations
constructed for her. The automatic control system techno
logy has a
big role in easing the burden of daily life, and make them more
luxury. automatic control applications in most appliances, such as:
air conditioning and stoves, washing machines, etc.Automated
control concepts has been used in various areas of knowledge such
as biology, economics, sociology, medicine and education .
DC-DC converter is one of the most essential component for
efficient utilization in renewable energy sources.
The main goal of this paper is to use Maximum power point
tracking (MPPT) system and buck-boost DC/DC converter in the
photovoltaic (PV)
system to maximize the (PV) output power,
irrespective of the temperature and irradiation conditions.
This research deals with the modeling of a Multi-Layers Feed Forward Artificial Neural
Networks (MLFFNN), trained using Gradient Descent algorithm with Momentum factor &
adaptive learning rate, to estimate the output of the neural network correspon
ding to the
optimal Duty Cycle of DC-DC Boost Converter to track the Maximum Power Point of
Photovoltaic Energy Systems. Thus, the DMPPT-ANN “Developed MPPT-ANN”
controller proposed in this research, independent in his work on the use of electrical
measurements output of PV system to determine the duty cycle, and without the need to
use a Proportional-Integrative Controller to control the cycle of the work of the of DC-DC
Boost Converter, and this improves the dynamic performance of the proposed controller to
determine the optimal Duty Cycle accurately and quickly. In this context, this research
discusses the optimal selection of the proposed MLFFNN structure in the research in terms
of determining the optimum number of hidden layers and the optimal number of neurons in
them, evaluating the values of the Mean square error and the resulting Correlation
Coefficient after each training of the neural network. The final network model with the
optimal structure is then adopted to form the DMPPT-ANN Controller to track the MPP
point of the PV system. The simulation results performed in the Matlab / Simulink
environment demonstrated the best performance of the proposed DMPPT-ANN controller
based on the MLFFNN neural network model, by accurately estimating the Duty Cycle and
improving the response speed of the PV system output to MPP access, , as well as finally
eliminating the resulting oscillations in the steady state of the Power response curve of PV
system compared with the use of a number of reference controls: an advanced tracking
controller MPPT-ANN-PI based on ANN network to estimate MPP point voltage with
conventional PI controller, a MPPT-FLC and a conventional MPPT-INC uses the
Incremental Conductance technique INC
