Design and Experimentation of FPGA-Based Soft-Switched Interleaved Boost Converter for Telecommunication System

This paper proposes the design and experimentation of digital control of soft-switched interleaved boost converter using FPGA for Telecommunication System. The switching devices in the proposed converter are turned on and off with Zero Voltage Switching (ZVS) and Zero Current Switching (ZCS) respectively. The circuit is operated in Continuous Conduction Mode (CCM) with various load ranges having duty cycle of more than 50%. The proposed converter is studied by developing the simulation module in MATLAB/SIMULINK. A PI controller is designed and implemented in FPGA to obtain a regulated DC output for line and load variations. Simulation and experimentation results are verified with a prototype development of the proposed converter. The results indicate that the converter performance is enhanced with closed loop control.

A Correlative Study of Perturb and Observe Technique and GA-RBF-NN Method Supplying a Brushless DC Motor

A comparative study is done in regards to the performance of the popular Perturb and Observe algorithm and the Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) algorithm, both incorporating the Interleaved Boost converter. The Perturb and Observe method (P&O) is inarguably the most commonly used algorithm as its advantages pertaining to its ease in implementation and simplicity enable to track the Maximum Power Point (MPP). However, it is absolutely unreliable when subjected to rapidly fluctuating irradiation and temperature levels. More importantly, the system has the tendency to swing back and forth about the Maximum Power Point without reaching stability. At this juncture, the implementation of the Genetic-Assisted Radial Basis Function (GA-RBF) algorithm helps the system achieve MPP at a shorter time when compared to the Perturb and Observe technique. The ever reliable and robust Levenberg-Marquardt algorithm is included along with the MPPT controller that minimizes the Mean Square Error (MSE) and aids in faster training of the neural network. This PV system drives a brushless DC motor (BLDC), employing rotor position sensors.

Interleaved high-gain boost converter powered by solar energy using hybrid-based MPP tracking technique

This paper presents a solar-powered interleaved high-gain boost converter(IHGBC)that increases voltage gain with fewer ripples in the output voltage in comparison to existing DC-DC converters.The goal of this research is to develop a hybrid-based maximum power point tracking(MPPT)approach with the combination of a flower pollination(FP)algorithm assisted with a perturb&observe(P&O)MPPT approach for solar photovoltaic(SPV)systems integrated with IHGBC.To ensure effective usage of both FP and P&O algorithms,this study incorporates and validates the hybrid-based MPPT approach.The proposed solar-powered IHGBC with a hybrid-based MPPT algorithm has been computationally modelled and simulated using MATLAB®and Simulink®for both uniform and non-uniform irradiation and analysed for voltage gain,ripples in the output waveforms and convergence time.The proposed hybrid-based MPPT is based on a number of flowers that forecast the initial global peak,assisted by P&O in the last stage for faster convergence to attain the maximum power point(MPP).As a result,the hybrid-based MPPT approach alleviates the computational issues encountered in P&O and FP-based MPP approaches.The proposed hybrid MPPT is compared with conventional MPPT for SPV and the results show that the solar-powered IHGBC using a hybrid-based MPPT technique has negligible oscillations of 0.14%with a high-voltage gain of 7.992 and a fast convergence rate of 0.05 seconds compared to individual P&O-based MPPT and FP-based MPPT techniques.The simulation results of the proposed MPPT with IHGBC outperform the conventional MPPT with high-gain converters.