Hamiltonian Control Law Based on Lyapunov–Energy Function for Four-Phase Parallel Fuel Cell Boost Converter

Thounthong, Phatiphat; Nahid‐Mobarakeh, Babak; Pierfederici, Serge; Mungporn, Pongsiri; Bizon, Nicu

doi:10.1109/icpei49860.2020.9431392

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…The electronic-thermal simulation was taken place to study the relation among driver's luminous flux and output current. Currently, a complete study has been held for investigating the impact of phosphor and humidity on moisture absorption, mechanical behavior, hygroscopic swelling, and thermal properties of silicone composite compared to pure silicone [9]. Recently, the machine learning (ML) technique was broadly utilized in different research fields.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning-Based LED Driver System

Ahamed¹,

Sukhi²

2023

J CIRCUIT SYST COMP

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Light emitting diodes (LEDs) have become an effective lighting solution because of the characteristics of energy efficiency, flexible controllability and extended lifetime. They find use in numerous lighting systems for residents, industries, enterprises and street lighting applications. The efficiency and trustworthiness of the LED systems considerably based on the thermal mechanical loading improved several degradation schemes and respective interfaces. The complication of the LED systems limits the theoretic interpretation of the core reasons for the luminous variation or the formation of the direct correlation among the thermal aging loading and the luminous output. Therefore, this paper designs a new hybrid Henry gas solubility optimization with deep learning (HHGSO-DL) algorithm for LED driver system design. The presented HHGSO-DL technique mainly concentrates on the derivation of empirical relationships among the design parameters, thermal aging loading and luminous outcomes of the LED product. In the presented HHGSO-DL technique, bidirectional long short-term memory (BiLSTM) algorithm is executed for examining the empirical relationship and its hyperparameters can be tuned by the HHGSO algorithm. In this work, the HHGSO algorithm is derived by the integration of traditional HGSO algorithm with oppositional-based learning (OBL) concept. The performance of the HHGSO-DL technique can be investigated on LED chip packaging and LED luminaire with thermal aging loading. The extensive results demonstrate the promising performance of the HHGSO-DL technique over other state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning-Based LED Driver System

Ahamed¹,

Sukhi²

2023

J CIRCUIT SYST COMP

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Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning based LED Driver System

Ahamed¹,

Sukhi²

2023

Preprint

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Light emitting diodes (LED) become an effective lighting solution because of the characteristics of energy efficiency, flexible controllability, and extended lifetime. They find use in numerous lighting systems for residents, industries, enterprises, and street lighting applications. The efficiency and trustworthiness of the LED systems considerably based on the thermal mechanical loading improved several degradation schemes and respective interfaces. The complication of the LED systems limits the theoretic interpretation of the core reasons for the luminous variation or the formation of the direct correlation among the thermal aging loading and the luminous output. Therefore, this article designs a new Hybrid Henry Gas Solubility Optimization with deep learning (HHGSO-DL) algorithm for LED driver system design. The presented HHGSO-DL technique mainly concentrates on the derivation of empirical relationships among the design parameters, thermal aging loading, and luminous outcomes of the LED product. In the presented HHGSO-DL technique, bidirectional long short-term memory (BiLSTM) algorithm is executed for examining the empirical relationship and its hyperparameters can be tuned by the HHGSO algorithm. In this work, the HHGSO algorithm is derived by the integration of traditional HGSO algorithm with oppositional based learning (OBL) concept. The performance of the HHGSO-DL technique can be investigated on LED chip packaging and LED luminaire with thermal aging loading. The extensive results demonstrate the promising performance of the HHGSO-DL technique over other state of art approaches.

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Improved Adaptive Hamiltonian Control Law for Constant Power Load Stability Issue in DC Microgrid: Case Study for Multiphase Interleaved Fuel Cell Boost Converter

et al. 2021

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The cascaded connection of power converters in a DC microgrid may cause instabilities. Indeed, power converters operating as external loads exhibit constant power load (CPL) behaviors. In this study, the design of the feedback controller of a multi–cell interleaved fuel cell (FC) step–up power circuit is based on the adaptive Hamiltonian control law. It includes two integral terms to confirm that there is no steady-state error in the DC bus voltage, and to guarantee the current balancing of each input inductor current. The design confirms that the desired equilibrium point is (locally) asymptotically stable by using the Lyapunov stability proof. The control approach is validated via digital simulations and experimental tests performed with a 2500 W FC converter supplied by an FC/reformer size of 2500 W and 50 V.

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Hamiltonian Control Law Based on Lyapunov–Energy Function for Four-Phase Parallel Fuel Cell Boost Converter

Cited by 3 publications

References 20 publications

Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning-Based LED Driver System

Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning-Based LED Driver System

Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning based LED Driver System

Improved Adaptive Hamiltonian Control Law for Constant Power Load Stability Issue in DC Microgrid: Case Study for Multiphase Interleaved Fuel Cell Boost Converter

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