A power control system in DC-DC boost converter integrated with photovoltaic arrays using optimized back propagation Artificial Neural Network

Jiteurtragool, Nattagit; Wannaboon, Chatchai; San-Um, Wimol

doi:10.1109/kst.2013.6512797

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…The relationship between phase shift angle and the transformer current is arrived. Jiteurtragool et al [49] proposed a power control system for DC-DC converter using ANN. Output voltage is regulated which is received from the PV array from changing ambient temperature and irradiance.…”

Section: Review On Controller For Ilbcmentioning

confidence: 99%

Survey on Photo-Voltaic Powered Interleaved Converter System

Bharathi¹

2020

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

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Renewable energy is the best solution to meet the growing demand for energy in the country. The solar energy is considered as the most promising energy by the researchers due to its abundant availability, eco-friendly nature, long lasting nature, wide range of application and above all it is a maintenance free system. The energy absorbed by the earth can satisfy 15000 times of today's total energy demand and its hundred times more than that our conventional energy like coal and other fossil fuels. Though, there are overwhelming advantages in solar energy, It has few drawbacks as well such as its low conversion ratio, inconsistent supply of energy due to variation in the sun light, less efficiency due to ripples in the converter, time dependent and, above all, high capitation cost. These aforementioned flaws have been addressed by the researchers in order to extract maximum energy and attain hundred percentage benefits of this heavenly resource. So, this chapter presents a comprehensive investigation based on photo voltaic (PV) system requirements with the following constraints such as system efficiency, system gain, dynamic response, switching losses are investigated. The overview exhibits and identifies the requirements of a best PV power generation system.

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Section: Review On Controller For Ilbcmentioning

confidence: 99%

Survey on Photo-Voltaic Powered Interleaved Converter System

Bharathi¹

2020

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

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“…Typically, photovoltaic systems employ a DC boost converter, which is a class of switching power regulator that provides operation of a PV array in its maximum power point (MPP) even during variation of solar irradiance and temperature [5].…”

Section: Fsm Model Of a DC Boost Convertermentioning

confidence: 99%

“…The converter operates in continuous conduction mode (CCM) and it works in two modes of operation, which are given by the operation state of the switch. The output variables are inductor current iL and the capacitor voltage vC [3], [5]. This state can be described by following differential equations [6]:…”

Section: A Mathematical Model Of a DC Boost Converter With Mppmentioning

confidence: 99%

“…The change of one state to the other is caused by the switching impulses generated by MPPT controller. Each state is described by different set of differential equations representing its dynamics, (4) and (5) or (6) and (7), respectively. For such cases, the use of finite state machine model is very suitable, because it enables to combine different representations of model's object dynamic operation very easily, just by defining appropriate number of states and transitions between them.…”

Section: B Modelling Of a DC Boost Convertermentioning

confidence: 99%

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Modeling of a DC boost converter behavior in PV system using finite state machines

Latkova

Bracinik

Bahernik

et al. 2015

2015 16th International Scientific Conference on Electric Power Engineering (EPE)

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This paper presents an approach to the modeling of a DC boost converter used in a small photovoltaic system, whose operation is controlled by the maximum power point tracking controller. The presented simulation model is capable of representing converter's dynamic operation during the changes of solar irradiance as well as speeding-up the simulation during a steady state converter's operation, when there is no change of solar irradiance. In order to achieve this functionality, a finite state machines model of the photovoltaic system was created. Used mathematical formulas and simulation results showing model's functionality are presented as well.

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“…Among the energy harvesting methods, photovoltaic has rapidly developed due to its relatively abundant energy source and inexpensive routine cost of operations [7][8][9]. Considering the installation cost, they are relatively expensive than their routine expenses [10][11][12][13], yet their lasting paybacks are huge. In the Off-Grid photovoltaic system design for an identified load, the power transfer of energy transducer, power converter, and energy storage specifications are optimally identified.…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of an Off-Grid Photovoltaic System with Duty Cycle Prediction Using Neural Network Controller

Zungeru

Duncan

Diarra

et al. 2021

JERA

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Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.

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A power control system in DC-DC boost converter integrated with photovoltaic arrays using optimized back propagation Artificial Neural Network

Cited by 9 publications

References 4 publications

Survey on Photo-Voltaic Powered Interleaved Converter System

Survey on Photo-Voltaic Powered Interleaved Converter System

Modeling of a DC boost converter behavior in PV system using finite state machines

Design and Simulation of an Off-Grid Photovoltaic System with Duty Cycle Prediction Using Neural Network Controller

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