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Register a new userImproving the efficiency of Solar Photovoltaic Power Systems using a Maximum Power Point Tracker Controller based on DC-DC Boost Converter
تحسين كفاءة نظم القدرة الشمسية الكهروضوئية باستخدام متحكم تتبع نقطة الاستطاعة العظمى المرتكز على مبدل رافع للجهد المستمر
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إيمان ديلانة( باحث )
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يعالج هذا البحث تحسين كفاءة نظم القدرة الشمسية الكهروضوئية باستخدام متحكم تتبع نقطة الاستطاعة العظمى، المرتكز في عمله على تقنيات تتبع تستخدم طريقة التحكم المباشر للتحكم في دورة عمل مبدل جهد مستمر لتحقيق عمل النظام الكهروضوئي عند نقطة الاستطاعة العظمى في ظل التغيرات الجوية المختلفة من شدة إشعاع شمسي و درجة حرارة محيطة. في هذا السياق، يتركز عملنا على محاكاة مكونات نظام توليد الطاقة من نظام كهروضوئي، مبدل رافع للجهد المستمر و متحكم MPPT في بيئة Matlab/Simulink. تتم محاكاة المتحكم MPPT باعتماد عدة خوارزميات: خوارزمية التوتر الثابت، خوارزمية الإضطراب و المراقبة و خوارزمية زيادة الناقلية، باستخدام تابع Embedded MATLAB function. أظهرت نتائج المحاكاة فعالية المتحكم MPPT في زيادة استطاعة النظام الكهروضوئي مقارنة مع عدم استخدام متحكم MPPT. كما أظهرت النتائج الأداء الأفضل لمتحكم MPPT المعتمد على خوارزمية الإضطراب و المراقبة و خوارزمية زيادة الناقلية، مقارنة مع خوارزمية التوتر الثابت في تتبع نقطة الاستطاعة العظمى للنظام في ظل التغيرات الجوية.
This research deals with improving the efficiency of solar photovoltaic (PV) power systems using a Maximum Power Point Tracker controller (MPPT controller), based in his work on the Maximum Power Point Tracking techniques via the direct control method. Which used to control the duty cycle of DC-DC Voltage Converter, to achieve the photovoltaic system works at a Maximum Power Point under different atmospheric changes of the solar insolation and ambient temperature. In this context, our work is focused on the simulation of the components of the power generating system, such as the photovoltaic system, DC-DC Boost Converter and a MPPT controller in Matlab/Simulink environment. The simulating of the MPPT controller was based on several algorithms such as: Constant Voltage algorithm, Perturb and Observe algorithm and Incremental Conductance algorithm by using Embedded MATLAB function. The simulation results showed the effectiveness of the MPPT controller to increase the photovoltaic system power compared with non-use of a MPPT controller. The results also showed the best performance of MPPT controller based on Perturb and Observe and Incremental Conductance algorithm, compared with constant voltage algorithm in tracking the Maximum Power Point under atmospheric changes.
References used
ESRAM, T.; CHAPMAN, P. Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion 22, 2007, 439–449
RAVI, N.; RAVI, M. A study on Maximum Power Point Tracking techniques for Photovoltaic systems. International Journal of Engineering and Technical Research. 3, 2015, 189-196
SHARMA, D.; PUROHIT, G. Hybrid Control Method for Maximum Power Point Tracking (MPPT) of Solar PV Power Generating System. Australian Journal of Basic and Applied Sciences. 8, 2014, 255-262
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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
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.
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.
In the following study we make a simulation of an independent
photovoltaic system connected to an (ohm
- unit of electrical resistance) load which consists of the following
parts:
(Photovoltaic Module - Converter dc- dc - Control system to
track
ing the maximum power point via MATLAB & Simulink
program)
Taking advantage of equations of Photovoltaic Module we chart
the graph and simulate curves of the Module.
We also simulate the converter –type Cuk- which gives higher or
lower voltage than input voltage but with reversed polarity.
We also make a comparison between the two systems tracking:
the first tracker is a traditional one and the second one is a system
in which it uses a fuzzy logic tracker.
The results of the comparison shows different capacities taking into
consideration the varieties of weather conditions of regular solar
radiation as well as the partial shadow.
Such results showed that fuzzy logic has got more capability to
harmonize with all conditions especially in cases of low solar
radiation and partial shadow.
This research deals with improving the efficiency of solar photovoltaic (PV) power
systems using a Fuzzy Logic Controller (FLC) for Maximum Power Point Tracking
(MPPT), to control the duty cycle of DC-DC Voltage Converter, to achieve the
photovolt
aic system works at a Maximum Power Point under different atmospheric
changes of the solar insolation and ambient temperature. In this context, this research
presents a new model for FLC developed in Matlab/Simulink environment. The proposed
model for the controller is based on the conventional Perturb and Observe (P&O)
technique. Where, in similar to the conventional P&O technique, the changes in the Power
and tension of photovoltaic power system, are considered as the input variables of the
proposed controller, while the output variable is the change in the duty cycle. The main
advantage of the developed controller FLC, based on the considering the change in the
duty cycle has a Variable Step Size, and directly related to the changes in the power and
tension of the Photovoltaic system. Which make it possible to overcome the problem of
fixed Step Size in the change of the duty cycle in the conventional MPPT- P&O Controller
based on P&O technique. The MPPT- P&O Fuzzy, works by a variable step size achieve a
fast speed response and high efficiency for tracking the MPP point under sudden and
rapidly varying atmospheric conditions, compared with the conventional MPPT- P&O. The
simulation results completed in Matlab/Simulink environment, showed the best
performance of developed MPPT- P&O Fuzzy controller in tracking the MPP by achieving
a better dynamic performance and high accuracy, compared with the use of the
conventional MPPT- P&O under different atmospheric changes.
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