DC-DC converter based power management for go green applications

W, Abitha Memala; Bhuvaneswari, C.; Shyni, S. M.; Sheeba, G. Merlin; Mahendra, Modi Surya; Jaishree, V

doi:10.11591/ijpeds.v10.i4.pp2046-2054

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…This most recent method, which was tested at (PSA), demonstrated the enormous potential of intelligent STSs. Additionally, the authors of the current study have obtained a patent for a novel method of Sun tracking devices [24]. The major finding of an extensive assessment and preliminary findings concur that the adoption of smart STSs is the most viable approach for the advancement of concentrated solar power and photovoltaic technology.…”

Section: Introductionsupporting

confidence: 57%

Dual axis solar tracking system for agriculture applications using machine learning

Somasundaram,

Dhandapani,

Kathirvel

et al. 2024

IJPEDS

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The three most basic amenities required for human survival are food, shelter and clothing. In today's tech-savvy generation, these have experienced a great deal of scientific advancement. Unfortunately, agriculture is still more man power-oriented. So they have to rely on the hit and trial method to learn from experience which leads to waste of time. In proposed work includes an automated system using dual axis solar tracking system and gives crop recommendation for different types of soil to yield maximum. The suggested system is a dual-axis solar tracker based on machine learning that is intended to considerably increase the effectiveness of energy harvesting. The approach makes use of the logistic regression algorithm (LR) to do this. This novel strategy tries to maximize the solar panel's ability to produce energy, leading to increased energy yields. The quality of soil is predicted by using suitable sensors for crop recommendation. The data’s are temperature, humidity, pH of soil, nitrogen, phosphorous & potassium in soil and rainfall in soil are considered. For crop recommendation six algorithms- SVM, KNN, Native Bays, Logistic Regression, Decision Tree classifier, Random Forest Classifier are applied and tested. It is found that random forest classifier gave us excellent results.

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Section: Introductionsupporting

confidence: 57%

Dual axis solar tracking system for agriculture applications using machine learning

Somasundaram,

Dhandapani,

Kathirvel

et al. 2024

IJPEDS

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“…The following flowchart Figure 3 describes the proposed general system power management strategy, which aims to control the battery when it charges or discharges [25]. The most important thing is to protect the battery in critical conditions and use the solar panel's maximum power, where there are four modes to supervise the system behavior.…”

Section: Battery Modelmentioning

confidence: 99%

Power management strategy based sugeno fuzzy logic rules in an electric wheelchair

Chakar

Abdelkhalek

Gasbaoui

et al. 2021

IJPEDS

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Power management in multi-power supply electrical systems to manage the general system behavior is essential to improve autonomy and efficiency. In this paper, a proposed fuzzy-logic power management-based sugeno rule is applied in a hybrid PV/battery electric wheelchair to ameliorate the battery life cycle and the overall autonomy. Besides, the increment conductance INC MPPT is used to maximize PVpower. The electric wheelchair's general topology comprises photovoltaic energy resources as the main source and the battery energy storage system device as the auxiliary source. This hybrid power source system supplied the electric wheelchair composed two permanent magnet DC motors controlled by a PI controller. MATLAB/Simulink program is used to implement the overall control scheme. The simulation results that were obtained and the detailed study demonstrate the feasibility and performance of this intelligent strategy.

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“…A further enhancement of the economic viability of installed photovoltaic (PV) systems has occurred because falling prices for PV modules and increased power conversion efficiency have helped drive down system costs. In 2014, almost 42 GW of new PV capacity was installed worldwide, bringing the global total to 180 GW [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

Independent swarm grid vehicle charging by a logical DC-DC converter with hybrid boundary conduction mode

Nagarajan,

Esakkiappan,

Kala Pandian

et al. 2023

IJEECS

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<p>A modern and expanding civilization now takes for granted that future environments will be clean, efficient, and carbon neutral. Energy-efficient electrical loads, such as v ehicle charging now often u sed in schools, companies, and gated communities. Recent articles have focused more on Swarm smart grid technology as a way to transmit power from rooftop solar installations. A major issue with the system is the necessity for inverters and other conversio n equipment. The mechanism consumes part of the power generated by this process. It is possible to utilize the DC supply to power the v ehicle chargers in smart houses, although some tweaks may be necessary. The charging Vehicle is a common task that can sh are by different converter by swarm network. This study demonstrates the feasibility of using a swam nano grid t o interconnect buildings inside a campus or other large business or residential complex, as well as within a gated community. The electricity gr id is monitored by the swam nano grid system, which eliminates the need for costly equipment. The DC - DC converter was being controlled directly by the simple logic controller. M ATLAB Simulink simulates the controlled converter under load and shows the performance of a bidirectional DC-DC converter using waveforms.</p>

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DC-DC converter based power management for go green applications

Cited by 6 publications

References 14 publications

Dual axis solar tracking system for agriculture applications using machine learning

Dual axis solar tracking system for agriculture applications using machine learning

Power management strategy based sugeno fuzzy logic rules in an electric wheelchair

Independent swarm grid vehicle charging by a logical DC-DC converter with hybrid boundary conduction mode

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