DC-DC Converter Design Using Big Data Methodology

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Description

With the rapid advancement in the technologies related to renewable energies such

as solar, wind, fuel cell, and many more, there is a definite need for new power con

verting methods involving

With the rapid advancement in the technologies related to renewable energies such

as solar, wind, fuel cell, and many more, there is a definite need for new power con

verting methods involving data-driven methodology. Having adequate information is

crucial for any innovative ideas to fructify; accordingly, moving away from traditional

methodologies is the most practical way of giving birth to new ideas. While working

on a DC-DC buck converter, the input voltages considered for running the simulations

are varied for research purposes. The critical aspect of the new data-driven method

ology is to propose a machine learning algorithm. In this design, solving for inductor

value and power switching losses, the parameters can be achieved while keeping the

input and output ratio close to the value as necessary. Thus, implementing machine

learning algorithms with the traditional design of a non-isolated buck converter deter

mines the optimal outcome for the inductor value and power loss, which is achieved

by assimilating a DC-DC converter and data-driven methodology.

The present thesis investigates the different outcomes from machine learning al

gorithms in comparison with the dynamic equations. Specifically, the DC-DC buck

converter will be focused on the thesis. In order to determine the most effective way

of keeping the system in a steady-state, different circuit buck converter with different

parameters have been performed.

At present, artificial intelligence plays a vital role in power system control and

theory. Consequently, in this thesis, the approximation error estimation has been

analyzed in a DC-DC buck converter model, with specific consideration of machine

learning algorithms tools that can help detect and calculate the difference in terms

of error. These tools, called models, are used to analyze the collected data. In the

present thesis, a focus on such models as K-nearest neighbors (K-NN), specifically

the Weighted-nearest neighbor (WKNN), is utilized for machine learning algorithm

purposes. The machine learning concept introduced in the present thesis lays down

the foundation for future research in this area so that to enable further research on

efficient ways to improve power electronic devices with reduced power switching losses

and optimal inductor values.