Articles published in IJEER


Exploiting PV System Performance: A Combined Approach Using MPPT, IoT, Cleaning, Cooling, and Neural Networks

This paper investigates the effects of three foremost methodologies—Maximum Power Point Tracking (MPPT), Internet of Things (IoT)-driven cleaning and cooling, and Neural Network Training (NNT)—on improving the efficacy of solar photovoltaic (PV) systems. Solar photovoltaic systems have considerable complications in sustaining maximum performance due to environmental conditions such as dust collection, temperature variations, and an insufficient energy management. A new control method is presented to challenge these difficulties, including MPPT, IoT-based cleaning and cooling, and NNT for the real-time optimization of PV systems. The simulation findings indicate a substantial increase in power production when both technologies are used together.

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An Optimal DC Microgrid for Hybrid Consumer Loads and Electric Vehicle Integration

This paper investigates the effects of three foremost methodologies—Maximum Power Point Tracking (MPPT), Internet of Things (IoT)-driven cleaning and cooling, and Neural Network Training (NNT)—on improving the efficacy of solar photovoltaic (PV) systems. Solar photovoltaic systems have considerable complications in sustaining maximum performance due to environmental conditions such as dust collection, temperature variations, and an insufficient energy management. A new control method is presented to challenge these difficulties, including MPPT, IoT-based cleaning and cooling, and NNT for the real-time optimization of PV systems. The simulation findings indicate a substantial increase in power production when both technologies are used together.

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Effective Energy Management System in Microgrid Employing Model Predictive Controller

The primary focus of this study is to develop an energy management system that regulates the energy transfers between the hybrid microgrid system and the loads connected to it, and the grid via MATLAB/Simulink so as to model the flow of energy. The secondary aim is to make recommendations aimed at the charging and discharging of what is referred to as the hybrid energy storage system (HESS). The results indicate that the proposed algorithm successfully carried out the required task of bridging the HESS charging to discharging ratio in relation to the different operating conditions as well as power management between the microgrid and the network. In this application, a stronger charging power might be employed on the HESS. It has been seen that the HESS is more likely to complete charging within a short time than the greater charging power.

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Fast Charging System of Electric Vehicle Using Optimized Isolated Multi-Port DC-DC Converter based on Modified Coati Optimization Algorithm

Once clean, renewable energy sources are used to charge the batteries in electric vehicles (EVs), the vehicles can produce zero gas emissions, greatly improving the environment. EVs and other distributed energy storage devices can be used in a smart microgrid to deliver energy to the loads throughout highest times, reducing the impact of load shading and improving the quality of the electricity. To achieve these goals of energy balance between EVs, the grid, and renewable energy sources, an isolated hybrid multiport converter is required. This paper develops an optimized isolated multi-port DC-DC converter for controlling power flow in multiple directions in an EV. This converter contains a dc-dc unidirectional converter, a bidirectional dc-dc converter, a triple active bridge (TAB), and a multi-port dual active bridge converter.

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Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment

Microgrids (MG) are small-scale energy systems that use distributed energy storage and sources. Hybrid microgrids are transforming energy management by incorporating various energy resources like wind, solar, and battery storage. Effective scheduling of this resource is vital to minimize the costs and maximize energy autonomy. Advanced scheduling algorithm optimizes the operation of hybrid microgrids, which dynamically adjusts the energy consumption and generation to satisfy the demand while ensuring power balancing. This scheduling strategy has been instrumental in improving the sustainability and resilience of MGS, which paves the way for an environmentally friendly and more reliable energy future. They can operate on islanded or grid-connected modes. The optimization of hybrid MG scheduling is paramount in the field of post-island management to ensure effective energy sustainability and distribution. Using metaheuristic approaches like simulated annealing or genetic algorithms allows the finetuning of scheduling parameters to increase energy utilization while reducing environmental impact and costs.

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EEG-Based Early Detection of Autism Using Convolutional Neural Networks

This Timely intervention and improved management of autism spectrum disorder (ASD) are contingent upon early detection. In this paper a novel method for early autism identification using EEG signals and convolutional neural networks (CNNs) is proposed. Preprocessing, wavelet transform, Discrete Cosine Transform (DCT) , energy and entropy function feature extraction, and CNN classifier classification are some of the phases in the suggested approach. EEG signals are first pre-processed to get rid of artifacts and noise. Then, to extract pertinent characteristics from the EEG signals, wavelet transform and DCT are used. For feature extraction, energy and entropy calculations are used to identify unique patterns suggestive of ASD. After then, a CNN classifier receives these features and divides them into two categories: Autism identified or normal identified.

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Optimized Energy Management for PV Hybrid Power Systems with DC Bus Voltage Control

The growing popularity of direct current (DC) power sources, energy storage systems, and DC loads has recently shifted the focus away from alternating current (AC) microgrids and towards DC-only systems. However, smart and energy-efficient building integration and effective microgrid administration are prerequisites. Direct current microgrids, which include solar modules as their principal power source, an energy storage device (battery), and an essential DC load, may have their energy consumption managed with the help of our study. Within the microgrid (MG) architecture, the DC-DC boost converter enables the PV module to operate in many modes, one of which is Maximum Power Point Tracking (MPPT). In order to link the battery and supercapacitor to the DC bus, the system also makes use of a DC-DC bidirectional converter.

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An Optimized Stationary Wavelet Fusion Technique for Image de-hazing

Nowadays, the amount of smoke and dust in the air is increasing significantly due to industrialization. The smoke and dust particles accumulate in the relatively dry air and cause haze in the surrounding area, impairs visibility. This haze also affects photography, which reduces the images' quality and looks unnatural. The hazy atmosphere affects even pictures taken with a cell phone in everyday life. There are many methods to remove this haze content from the image, but they have not yielded great results. The long-time and short-time shots constantly differed while attempting to eliminate atmospheric haze from the images. To solve this problem, a fusion rule was proposed to fuse the luminance and dark channel prior (DCP) methods. The transmission estimated with the DCP method contributes mainly to the foreground regions, while the luminance model deals with the celestial regions.

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Load modeling of electric bus charging station from data obtained through Phasor Measurement Units

While the increased adoption of electric vehicles (EVs) is a promising alternative to reduce CO2 emissions, it creates new challenges for the power grid due to increased energy demand and power quality (PQ) issues. These impacts vary depending on several factors such as the level of EV adoption, charging technology, network voltage level, charging patterns, charging station location, battery condition, and driving habits. Analyzing these impacts and developing solutions, such as characterizing the demand curve for charging stations and understanding EV charging patterns, is crucial to ensure a sustainable transition to an electrified EV future. A study using the ZIP load model that represents voltage dependence by combining constant impedance (“Z”), constant current (“I”), constant power (“P”) components, and phasor measurement units (PMUs) demonstrates the effectiveness of EV demand characterization. The importance of this aspect for grid stability and charging management is highlighted.

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Implementation of PI Controller of Hybrid DC Microgrid Energy Management

This paper presents energy management in DC Microgrid. Microgrids are a growing power generating source in remote areas than the utility grid. It can be operated as a standalone & grid-connected to serve the entities. This paper concentrates on the DC Microgrid and manages the energy within the system when the grid suffers from an outage of service, and unbalanced load conditions in the grid. During grid conditions, the buses connected to it should synchronize with the grid. The voltage in the buses is same, that is verified in this paper work through controllers to the converter are also discussed.

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