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Kamarudin, Muhammad Nizam; Rozali, Sahazati Md.; Jamri, Mohd Saifuzam

doi:10.11591/ijpeds.v12.i3

Cited by 18 publications

(3 citation statements)

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“…PSO is a global optimization algorithm based on swarm intelligence proposed by Kennedy et al in 1995 [27]. The PSO algorithm is a search algorithm that can be used for different complex problems [28][29][30][31][32][33]. It initializes a group of particles that do not have volume and mass first so that each particle is approximated as a feasible solution to the problem to be optimized.…”

Section: Pso Algorithmmentioning

confidence: 99%

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

Zhao,

Bian

2024

Applied Sciences

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In this paper, we aim to address the limited capacity of compensation devices by enhancing their utilization rate by applying the currents’ physical component (CPC) theory for reactive power optimization in three-phase four-wire systems. When reactive currents cannot be fully compensated for, we propose using CPC theory to generate reference currents for the compensation devices. Weight coefficients associated with different reactive current components are introduced, enabling flexible combinations of these independent current components. The maximum output amplitude of the three-phase current from the compensation device serves as a constraint condition, allowing for the calculation of reference currents under various compensation targets. Additionally, a reactive current optimization compensation scheme focusing on loss reduction is selected. The simulated annealing–particle swarm optimization (SA-PSO) hybrid algorithm is employed to solve the optimization mathematical model. The discussed calculations, current waveforms, and voltage waveforms are generated using the constructed mathematical model and then used for a theoretical explanation. The simulation verifies the feasibility of the proposed method.

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Section: Pso Algorithmmentioning

confidence: 99%

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

Zhao,

Bian

2024

Applied Sciences

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“…This allows you to use the computing resources of any computer systems without reference to a certain area. An active cooling system for a solar panel with a real time energy monitoring system with Internet-of-Things (IoT) facility was developed and discovered that the automatic active cooling system improves the efficiency of the solar panel due to the fact when the temperature is maintained by the cooling apparatus, the output power can be obtained as optimum as it can [24]. One more example of IoT monitoring PV System Optimization is the implementation of IoT monitoring on an On-Grid PV system installed on the rooftop of a hospital environment [25].…”

Section: Introductionmentioning

confidence: 99%

Neural Network based Active Cooling System with IoT Monitoring and Control for LCPV Silicon Solar Cells

et al. 2023

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One of the methods of increasing output power of solar cells is increasing the concentration ratio using lenses and mirrors. Due to the temperature increase of silicon solar cells with concentrating lenses the system must be provided with an active cooling system. PV systems cooled with active or passive cooling methods using air, water or other substances are not taking into account the relationship between the power of solar radiation and operating power of the pump used in the cooling system, which is sufficiently complicated and little studied. This paper proposes a new active energy-efficient cooling system for silicon LCPV (Low Concentrating Photovoltaic). The idea of the proposed cooling system is to make the pump operate as efficiently as possible relative to the power of solar radiation and initial temperature of the heated solar cell. Performed experiments show that at fixed temperature and fixed solar radiation there is the most optimal pump operating power. Neural network is used to find this optimal pump power. Using neural networks requires large computing resources and development software which is not often available on local control computing devices nearby solar power plants. IoT (Internet of Things) technologies allow not only remote monitoring and control, but also forecasting the consumption of the cooling system depending on the current temperature and solar radiation power. Thus, energy efficiency of the cooling system is improved using neural networks and IoT technologies. The simulation of the cooling system operation was performed using various algorithms of the cooling system operation. The proposed active cooling system for silicon LCPV using IoT technologies allows the power consumption to be reduced by 60% compared to algorithms based on the threshold value of temperature.INDEX TERMS IoT monitoring and control, neural networks, Bi-LSTM, XGBoost, LCPV silicon solar cells, active cooling system.BATYRBEK ZHOLAMANOV was born in Aral, Kyzylorda, Kazakhstan, in 1999. He received the B.S. and M.S. degrees in radio engineering, electronics, and telecommunications from Al-Farabi Kazakh National University, in 2020 and 2022, respectively, where he is currently pursuing the Ph.D. degree in radio engineering, electronics, and telecommunications. He was a Research Assistant with the

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“…Specifically, in an EV context, a well-designed HESS can improve the efficiency of the power supply system and extend battery life by reducing battery stress [5]. Moreover, the HESS facilitates instantaneous power requirement compatibility and the capability to deliver substantial energy over long distances [6].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Low Pass Filter Cut-off Frequency for Energy Management in Electric Vehicles with Hybrid Energy Storage Systems

Maghfiroh,

Wahyunggoro,

Cahyadi

2023

MMEP

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Mitigating pollution in the transportation sector necessitates the deployment of zeroemission solutions, such as electric vehicles (EVs). One significant challenge with EVs is the limited lifespan of the battery, a key and costly component. To circumvent this issue, a potential solution lies in the integration of batteries with supercapacitors to create a Hybrid Energy Storage System (HESS). This combination can notably decrease the peak current of the battery, thereby prolonging its lifespan, and ultimately, contributing to the long-term cost-effectiveness of EVs. A critical component of the HESS is the Energy Management Strategy (EMS), tasked with optimizing energy distribution. A Low-Pass Filter (LPF) serves as an uncomplicated, real-time EMS. The current study introduces a novel approach for determining the optimal cut-off frequency of the LPF, termed the Ragone Plot with Fine Tuning (RPFT). The Ragone plot provides a general cut-off frequency for the battery and drive cycle, while fine-tuning is employed to optimize it. Simulation results reveal that the RPFT method outperforms the Fast Fourier Transform (FFT) method, thereby proving its efficacy. The application of RPFT resulted in a reduction of battery peak current and battery current root mean square (BCRMS) by up to 29.80% and 9.99%, respectively. This study offers valuable insights for improving energy management in electric vehicles and underscores the potential of the RPFT method in extending battery lifespan and enhancing the costeffectiveness of EVs.

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Cited by 18 publications

References 0 publications

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

Neural Network based Active Cooling System with IoT Monitoring and Control for LCPV Silicon Solar Cells

Optimizing Low Pass Filter Cut-off Frequency for Energy Management in Electric Vehicles with Hybrid Energy Storage Systems

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