Multivariable-Feedback Sliding-Mode Control of Bidirectional DC/DC Converter in DC Microgrid for Improved Stability with Dynamic Constant Power Load

Yin, Yuqiang; Mao, Jingfeng; Liu, Runda

doi:10.3390/electronics11213455

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…This is because customers may need to replace their batteries during the life of their phone, or they may want to have an extra battery for situations when they cannot charge their phone [16]. Consumers have little pressure to upgrade their batteries unless they fail or need a larger battery [17]. Regarding bidirectional DC-DC converters, they can charge and discharge batteries and connect them to the D.C. microgrid to effectively utilize distributed power sources [18].…”

Section: Literature Reviewmentioning

confidence: 99%

The Empirical Analysis, Mathematical Modeling, and Advanced Control Strategies for Buck Converter

Ullah,

Rizvi,

Khatoon

et al. 2024

IEEE Access

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The DC-DC converters are essential in power electronics as they maintain a stable output voltage even when there are changes in input voltage and load current. This study introduces an advanced Proportional-Derivative (PD) compensator for buck converters. The compensator enhances stability and transient responsiveness by employing a unique modulation technique that has not been previously applied in this context. The proposed method entails applying an input of 28 volts, which yields an output amplification of 15 volts, even in the presence of interference. The small signal transfer function of the buck converter is meticulously derived, considering the converter's dynamic behavior to achieve exceptional results. The transfer function comprehensively explains the intricate relationship between the input and output voltages, providing the theoretical basis for our distinctive control approach. The MATLAB code accurately generates the Bode diagram of the buck converter by employing the small signal transfer function. The graph illustrates the frequency response of the converter, a crucial factor in enhancing the stability and quality of the output voltage. The proposed research is substantiated by mathematical data shown through several simulated figures, distinguishing it from conventional methodologies. The implemented research achieves the maximum efficiency exceeding 95% with a minimum ripple factor.

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Section: Literature Reviewmentioning

confidence: 99%

The Empirical Analysis, Mathematical Modeling, and Advanced Control Strategies for Buck Converter

Ullah,

Rizvi,

Khatoon

et al. 2024

IEEE Access

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show abstract

“…Thus, energy management and autonomous control strategies are the focus of research in energy management strategies [6][7][8]. Given the challenges of intermittent PV power generation, load fluctuation, and the economy of microgrid systems, it is necessary to realize the control of multiple objectives, i.e., increasing the life of ESS devices [9], maximizing the utilization of new-energy power generation, stabilizing bus voltage in a certain range [10], and minimizing the economic cost of microgrid operation [11]. In general, the control hierarchy is divided into three layers.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Energy Management of DC Microgrid with Photovoltaic Power Generation and Energy Storage for 5G Base Station

Han,

Lin,

2024

Sustainability

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For 5G base stations equipped with multiple energy sources, such as energy storage systems (ESSs) and photovoltaic (PV) power generation, energy management is crucial, directly influencing the operational cost. Hence, aiming at increasing the utilization rate of PV power generation and improving the lifetime of the battery, thereby reducing the operating cost of the base station, a hierarchical energy management strategy based on the improved dung beetle optimization (IDBO) algorithm is proposed in this paper. The first control layer provides bus voltage control to each power module. In the second control layer, a dynamic balance control strategy calculates the power of the ESSs using the proportional–integral (PI) controller and distributes power based on the state of charge (SOC) and virtual resistance. The third control layer uses the IDBO algorithm to solve the DC microgrid’s optimization model in order to achieve the minimum daily operational cost goal. Simulation results demonstrate that the proposed IDBO algorithm reduces the daily cost in both scenarios by about 14.64% and 9.49% compared to the baseline method. Finally, the feasibility and effectiveness of the proposed hierarchical energy management strategy are verified through experimental results.

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“…Power LEDs were the most effective light source in the marketplace, and it has lifetime. Power LEDs were utilized in many lighting mechanisms, like street lighting, suburban enterprise, and industrial applications [1]. LED driver utilizes dc-dc converters for maintaining constant LED current since it is destructed when an overcurrent circulated over them.…”

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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Multivariable-Feedback Sliding-Mode Control of Bidirectional DC/DC Converter in DC Microgrid for Improved Stability with Dynamic Constant Power Load

Cited by 5 publications

References 25 publications

The Empirical Analysis, Mathematical Modeling, and Advanced Control Strategies for Buck Converter

The Empirical Analysis, Mathematical Modeling, and Advanced Control Strategies for Buck Converter

Hierarchical Energy Management of DC Microgrid with Photovoltaic Power Generation and Energy Storage for 5G Base Station

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

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