Design and Analysis of Passive LC<sup>3</sup>Component Boost Converter

Rahman, Mohammad Dehan; Faisal, Fahim; Nishat, Mirza Muntasir; Shagor, Md. Rafid Kaysar

doi:10.1109/mascon51689.2021.9563364

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…Machine learning has been a crucial aspect in numerous significant domains for researchers [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64]. In recent years, there has been a surge of interest in using machine learning algorithms to predict the critical temperature of superconductors.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning based Investigative analysis for predicting critical temperature of superconductors

Shams,

Ratul,

Naf

et al. 2023

EAI Endorsed Trans AI Robotics

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INTRODUCTION: Ever since the initial discovery of superconductivity, the fundamental concept and the complex relationship between critical temperature and superconductive materials have been subject to extensive investigation. However, identifying superconductors that exhibit such behavior at normal temperatures remains a significant challenge, and there are still significant gaps in our understanding of this unique phenomenon, particularly regarding the fundamental criteria used to estimate critical temperature.OBJECTIVES: To address this knowledge gap, a plethora of machine learning techniques have been developed to model critical temperatures, given the inherent difficulty in predicting them using traditional methods.METHODS: Additionally, the limitations of the standard empirical formula in determining the temperature range require the development of more advanced and viable methods. This article presents an investigative analysis on the performance achieved by different supervised machine learning algorithms when used with three different feature selection techniques.RESULTS: The stacking model used in this work is found to be the best performer among all the algorithms tested, as reflected by the Root Mean Squared Error (RMSE) of 9.68, R2 score of 0.922, Mean Absolute Error (MAE) score of 5.383, and Mean Absolute Percentage Error (MAPE) score of 4.575.CONCLUSION: Therefore, it is observed that ML algorithms can contribute significantly in the domain of predictive analysis of modeling critical temperatures in superconductors and can assist in developing a robust computer-aided system to aid the education personals and research scientists to further assess the performance of the ML models.

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Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning based Investigative analysis for predicting critical temperature of superconductors

Shams,

Ratul,

Naf

et al. 2023

EAI Endorsed Trans AI Robotics

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“…An ample amount of literature exists on the successful executions of the Bio-Inspired Algorithms in the field of power converters professed by many researchers [11][12][13][14][15][16][17][18][19][20]. One of such promising instances of work has been witnessed with Machine learning (ML) algorithms [21][22][23][24][25][26][27][28][29], artificial intelligence [30][31][32][33][34][35][36][37][38][39], and different applications of ML in healthcare sectors [40][41][42][43][44][45][46][47][48][49], and so many other cases [50][51][52][53][54][55][56][57][58][...…”

Section: Introductionmentioning

confidence: 99%

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

Shagor,

Faisal,

Nishat

et al. 2023

EAI Endorsed Trans AI Robotics

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This paper represents the idea of implementing bio-inspired algorithms in designing an optimized PID controller to investigate the stability and improve the performance of the closed-loop CUK converter. Bio-inspired algorithms (BIA) such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and, Artificial Bee Colony (ABC) are stochastic optimization techniques and have been increasingly applied to attain optimal solution for designing and optimizing power converters in current times. The closed-loop transfer function of the CUK converter has been developed by the State Space Average (SSA) technique. This paper assesses both the cases of converter stability when it is integrated with the conventional PID controller and the BIA-PID controllers (FA-PID, PSO-PID, ABC-PID) and eventually compares the outcomes from all the controllers. For examining the stability of the system, three objective functions (IAE, ITAE, and ISE) and various performance specifications such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated. MATLAB and Simulink are used to carry out the simulations meticulously. Hence, a comparative analysis is illustrated in this paper to state a clear-sighted evaluation of the performances.

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“…Buck, Boost, and Buck-boost can perform step down, step up, and step up-down actions, respectively. However, some higher order converters like Zeta, Cuk, and SEPIC converters, allow power when the demand required a low input voltage and a high output voltage [4][5][6][7][8][9]. In correlation with Cuk and SEPIC converters, the Zeta converter is a nonlinear, noninverting fourth-order converter that can operate as buck-boost-buck with respect to input and boostbuck-boost with respect to energy output.…”

Section: Introductionmentioning

confidence: 99%

Studying the Applicability of Swarm Intelligence in Designing Optimized PI Controller for DC-DC Zeta Converter: A MATLAB-Based Approach

Shagor

Sami

Nishat

et al. 2022

Mathematical Problems in Engineering

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Studying the stability of power converters and improving the performance is a major concern for researchers in the domain of power electronics. In this context, the DC-DC Zeta converter is studied in this paper and the closed-loop operation is comprehensively investigated by employing swarm intelligence (SI) algorithms with a view to design an optimized proportional-integral (PI) controller. These algorithms have been increasingly used to develop and optimize power converters in recent years. The state-space averaging technique was used to design the converter’s closed-loop transfer function. Hence, the traditional and SI algorithm-based PI controllers are inspected, and comparative analysis is presented. Four objective functions termed as integral absolute error, integral time absolute error, integral square error, integral time squared error, gain values, and different performance parameters such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated to examine the stability of the system. Furthermore, eigenvalues have been analyzed for determining the stability of the system extensively. Finally, a detailed comparative study is shown to provide a detailed evaluation of the performances where ant colony optimization for continuous domains (ACOR)-based PI controller has shown promising results than other SI-based controllers in terms of percentage of overshoot (2.27%), rise time (1.54 μs), and settling time (0.103 μs). All the simulation results and analysis are obtained using the MATLAB-Simulink.

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Design and Analysis of Passive LC³Component Boost Converter

Cited by 13 publications

References 19 publications

A Machine Learning based Investigative analysis for predicting critical temperature of superconductors

A Machine Learning based Investigative analysis for predicting critical temperature of superconductors

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

Studying the Applicability of Swarm Intelligence in Designing Optimized PI Controller for DC-DC Zeta Converter: A MATLAB-Based Approach

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Design and Analysis of Passive LC3Component Boost Converter

Cited by 13 publications

References 19 publications

A Machine Learning based Investigative analysis for predicting critical temperature of superconductors

A Machine Learning based Investigative analysis for predicting critical temperature of superconductors

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

Studying the Applicability of Swarm Intelligence in Designing Optimized PI Controller for DC-DC Zeta Converter: A MATLAB-Based Approach

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Design and Analysis of Passive LC³Component Boost Converter