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Monday, 28 May 2018

Neural Network Based High-Performance Double Boost DC-DC Converter in Using Renewable Energy System

Abstract— Difference types of DC-DC converter are used in various electronic devices and applications for so many years. But conventional converter cannot afford in high voltage and high current applications. Many researchers have been tried to full-fill the requirements. In this paper, dual stage double boost DC-DC converter is used for renewable energy system. To obtain a control method has the best performance under any condition is always demand. Voltage mode control technique is applied to achieve the constant high output voltage with the help of advanced controller. The main objective of this paper is the study of Neural Network Controller (NNC) under the response of different parameters of proposed converter using Matlab/Simulink Software.

 Keywords — Double boost DC-DC converter, Electronic device, Neural network controller (NNC), Renewable energy system, Voltage mode control.

INTRODUCTION

 DC-DC converters are used in many different applications like electric vehicles, distributed DC systems, electric traction, machine tools, fuel cell, special electrical machine drives and solar PV based applications. DC-DC converter can convert low input voltage to high output voltage (required voltage) [1]. But, the basic boost topology does not provide a high boost factor. This has led to many proposed topologies. If a very high voltage gain is required, it may be more beneficial to use of two or more series connected (cascaded) boost converters. This approach gives some advantages, but it creates new challenges in the same time. Main advantages include a high voltage gain, a good power decoupling between the output and input, better utilization of semiconductors, presence of an intermediate DC bus. Main drawbacks are more complex circuit, more complex controls and a potential stability problem [2]. DC-DC boost converter is specialized making control in this paper. Schematic diagram of DC-DC boost converter in solar energy application system is described in Fig. 1. The classical control methods employed to design the controllers for double boost converter depends on the operating point so that it is very difficult to select control parameters because of the presence of parasitic elements, time varying loads and variable supply voltages.

Conventional controllers require a good knowledge of the system and accurate tuning in order to obtain the desired performances [5]. Neural Network Controller (NNC) are gaining popularity in modeling, identification and control of power electronic converters [6]. The linear controllers such as Proportional (P), Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) control were widely used to control the active performance of the converter [4]. However, the linear control of converter is not sufficient to face the changes in line voltage or load current. Hence non-linear controlling techniques such as Fuzzy Logic Control (FLC), Neuro-Fuzzy Logic Control (NFLC), Adaptive Neural Network (ANN) and Genetic algorithm (GA) controlling techniques are implemented to increase the performance of the converter [7]. In this regard, the objectives of this study are to propose a simple and efficient method of advanced converter based on the properties of simple boost converter topology and to analyze the performance of proposed method for the voltage loop. The main objectives are to make the current in the inductor to track and to regulate the output dc voltage to desired reference voltage. The designed controllers have been tested using MATLAB/Simulink. The rest of the paper is organized as follows. Double boost DC-DC converter is presented in section 2. Neural network controller for proposed system is described in section 3. Simulation controlled results for proposed converter are presented and discussed in section 4 followed in by conclusion section 5.

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