A Solar - Powered charger with Neural Network Implemented on FPGA

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 11, 2016 | ISSN (online): 2321-0613

A Solar – Powered Charger with Neural Network Implemented on FPGA 1,2

Mayuri Vasantrao Patil1 Prof.A.P.Hatkar2 Department of Electronics & Telecommunication Engineering 1,2 Sir Vishwasherya College of Engineering, Nasik

Abstract— Neural network may be a branch of the final field of intelligent management that is predicated on the conception of computer science. Neural controller will operate at totally different completely different} conditions of load current at different orbital periods with none standardization such just in case of pelvic inflammatory disease controller. During this a lift convertor is employed.

The controlled boost convertor is employed as Associate in tending interface between electrical phenomenon (PV) panels and also the hundreds connected to them. It converts any input voltage among its operational vary into a continuing output voltage that's appropriate for load feeding. Associate in Nursing ANN is trained employing a back propagation with Levenberg–Marquardt formula. Neural network controller design offers satisfactory result with little variety of nerve cells, thence battery in terms of memory and time square measure needed for neural network controller implementation. To implement the neural network into hardware style, it's needed to translate generated mode into device structure. VHDL language is employed to explain those networks into hardware. Satellite earth stations that settled in remote square measure as are the foremost necessary application of renewable energy. The results indicate that the planned management unit exploitation ANN is with success used for dominant the satellite earth station grid. With low exactitude artificial neural network style, FPGAs have smaller size and better speed for real time application than the DSP and VLSI chips the most effective validation performance is obtained for minimum mean sq. error. The regression between the network output and also the corresponding target is adequate to ninety nine which implies a high accuracy. The result shows that smart agreement between MATLAB. Key words: Powered Charger with Neural Network, FPGA

We have a tendency to have an interest in building a special category of hardware surroundings, i.e. FPGA primarily based reconfigurable computing surroundings for implementing ANNs. FPGA supply speed equivalent to dedicated and stuck hardware systems for parallel algorithmic program acceleration, whereas like a computer code implementation, retentive high degree of flexibility for device reconfiguration as application demands.

Implementation of ANNs falls into 2 categories: Hardware implementation and computer code implementation. ANNs area unit enforced in computer code, area unit trained and simulated on general ordered computers for emulating wide selection of neural networks models.

Computer code implementations supply flexibility. but hardware implementations area unit essential for pertinence conjointly for taking the advantage of ANN’s inherent correspondence [2]. We have a tendency to have an interest building a special category of hardware surroundings, i.e.

FPGA primarily based reconfigurable computing surroundings for implementing ANNs. FPGA supply speed equivalent to dedicated and stuck hardware systems for parallel algorithmic program acceleration, whereas like a computer code implementation, retentive a high degree of flexibility because the application demands [3]. With the introduction of FPGAs, it's possible to produce custom hardware for application specific procedure style. The changes in styles in FPGAs are accomplished inside a number of hours, and therefore end in vital style cycle and savings in price.

FPGAs have higher speed and smaller size for real time application than the VLSI and DSP chips. So, several researchers have created nice efforts on the conclusion of neural network (NN) victimization FPGA technique [3, 4] II. PROPOSED BLOCK DIAGRAM

I. INTRODUCTION In gift case the matter of fossil energy depletion becomes additional severe thus electrical phenomenon has received additional interest because the energy supply. electrical phenomenon refers the conception of conversion of daylight into current via a photovoltaic cell [1].The output current vs. voltage curve of PV array shows a non-linear I-V characteristic that depends on environ-mental conditions like star irradiance and temperature. During this curve, there's a definite operational purpose at that the PV array produces most power that is understood as most outlet (MPP).

Artificial neural networks (ANNs) area unit helpful to implement nonlinear, time-varying input-output mapping.

Artificial Neural Networks (ANNs) will solve nice kind of issues in areas of, image process, pattern recognition and medical diagnostic. The biologically galvanized ANNs area unit parallel and distributed information science systems.

ANN is associate degree information science system aims to simulate human brain's design and performance. it's currently a well-liked subject in several fields conjointly a tool in several areas of downside finding.

The term electrical refers to the phenomenon involving the conversion of daylight into voltage via a cell. Therein on top of diagram solar array is nothing however PV module. Solar array electrical signal is employed for analog to digital conversion; its power is to be used for boost convertor to make star battery-powered battery charging system [6]. The boost convertor circuit employed in the battery charging mechanism includes of 1 electrical device (L=15mH), two capacitors (C1=2200µF, C2=100µF), and 2 Schottky diodes D1 and D2.A power MOSFET BUZ11 operational at 46kHz is employed as a switch device S. [7] A business –grade battery bank with rated at 12V, 5Ahr is employed for the facility storage. Its chosen love the electrical device.

Fig. 1: Proposed Block Diagram In our style, the FPGA performs 2 main functions.

First, it determines duty magnitude relation D for the

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A Solar – Powered Charger with Neural Network Implemented on FPGA (IJSRD/Vol. 3/Issue 11/2016/049)

operation of switch S in such the simplest way that the in operation purpose of the solar battery may be controlled toward the most wall socket. It conjointly detects the battery voltage to avoid battery overcharging.

III. THE ELECTRICAL CONVERTER MODEL The PV arrays Associate in tending batteries manufacture DC and so once the stand alone PV system contains an AC load, a DC/AC conversion is needed. Associate in Nursing convertor} may be a converter wherever the ability flows from DC at its input and AC at its output. The role of the electrical converter is to stay constant voltage on the AC aspect and to convert the input power Pin into the output power Pout with the most effective potential potency. The electrical converter is characterized by an influence dependent potency in given by equation [12] đ?‘ƒđ?‘œđ?‘˘đ?‘Ą đ?‘‰đ?‘Žđ?‘? đ??źđ?‘Žđ?‘? đ?‘?đ?‘œđ?‘ (∅) ᜯđ?‘–đ?‘› = = ‌(1) đ?‘ƒđ?‘–đ?‘› đ?‘‰đ?‘‘đ?‘? đ??źđ?‘‘đ?‘?

Wherever Idc is that the current needed by the electrical converter from the DC supply so as to stay the rated voltage on the AC aspect, Vdc is that the input voltage to the electrical converter delivered by the DC supply, holiday and Iac square measure the output voltage and current, severally IV. ARTIFICIAL NERVE CELL NETWORK The structure of the Multi-Layer Perceptron (MLP) planned for dominant the charge current of battery is shown in Figure three. It consists of 3 layers. [12] the primary one or input layer has 2 inputs as follows:  Error: error signal between the generated and cargo current.  Tair: close temperature.

The second layer, conjointly known as the hidden layer, has 2 neurons or nodes. Finally, the last layer is termed the output layer, has just one node. It represents the worth of modification in battery charge current. The coaching is completed by the Levenberg-Marquart back propagation rule.

Fig. 3: MATLAB view of NN

Fig. 2: Architecture of NN controller for power system Fig. 4: Mean Square error of NN V. MATLAB-SIMULINK RESULTS In order to run the simulation for the world station facility, the star isolation and air temperature knowledge area unit required. Radiation knowledge area unit vital and essential for style, sizing, and simulation of PV power systems. The battery delivers the desired power to load throughout night periods whereas it charges in sunny periods. Figure four depicts the mean sq. error which might be outlined because the average square distinction between the output and therefore the target. [9] it's clear from the figure that the result's cheap owing to tiny mean sq. error will be obtained from NNC, The network response analysis is indicated in Fig. 5.[8] It indicates the regression (R) that measures the correlation between output and target. the worth of (R) is almost zero.99 you which ones means the output tracks the targets all right for coaching, testing, and validation.

Fig. 5: NN Regression

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A Solar – Powered Charger with Neural Network Implemented on FPGA (IJSRD/Vol. 3/Issue 11/2016/049)

management applications. Therefore, the projected management technique of earth station power grid is given.

Conjointly shows PWM waveforms offers Duty Cycle amendment as intensity of PV rays amendment.

REFERENCES

Fig. 6: RTL view of synthesized architecture of earth station NNC using XILINX Spartan 3

Fig. 7 Final Output of NN VI. CONCLUSIONS In this paper, implementation of NN controller victimization hardware description language VHDL is performed. The primary reason for the employment of VHDL is to extend the productivity by creating it straightforward to make and use libraries of usually used VHDL modules. Another excuse to fast development in electronic style automation (EDA) tools and in target technologies. By victimization VHDL will greatly improve your possibilities of getting in additional advanced tools while not having to get into your circuit descriptions. the power to retarget circuits to new styles of device targets are going to be improved by employing a customary style entry methodology. This enforced digital neural controller was designed to be easy therefore to form quick method and not need giant hardware resources. the combination between MATLAB and VHDL is achieved. The Neural network of digital neurons will then be synthesized into digital hardware employing a industrial synthesis tool.

[11, 12] In general, it's shown that implementation of neural networks victimization FPGAs. ANN is trained employing a back propagation with Levenberg–Marquardt rule victimization MATLAB–SIMULINK. A 2-2-1 feed forward neural network is obtained by MATLAB-SIMULINK for the planet station power grid controller. The results provide a sensible agreement between VHDL and MATLAB software system. The VHDL-FPGA combination is shown to be a really powerful embedded system style tool, with low value, high dependability. Nowadays, extended attention has been centered on use of ANN on system modeling and

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