Indonesian Journal of Electrical Engineering and Computer Science
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Design of high-efficiency microinverter for a photovoltaic system with low harmonic distortion
Get PDFThis article presents the design of a modular pure sine wave microinverter with a high-efficiency maximum power point tracking (MPPT) regulator for photovoltaic (PV) systems. The design starts with a DC/DC buck-boost chopper regulator, simulated using the perturb and observe (P&O) algorithm. Next, a high-frequency DC/AC conversion stage is implemented using a toroidal transformer to achieve various voltage levels and isolated power sources. Finally, a 27-level multilevel inverter is designed to produce a pure sine wave with minimal total harmonic distortion (THD). Simulation results indicate that the microinverter achieves a total efficiency of 90% and produces a pure wave output with 3% harmonic distortion. Compared to commercial solutions, the proposed design enhances efficiency while integrating key components. Additionally, the system maintains a cost-effectiveness and directly proportional to its energy efficiency, making it a viable and cost-effective solution for PV energy conversion
An improved hybrid AC to DC converter suitable for electric vehicles applications
Get PDFThis paper introduces a novel hybrid AC-DC converter designed for various applications like DC micro-grids, Electric Vehicle setups, and the integration of renewable energy resources into electric grids. The suggested hybrid converter involves a diode bridge rectifier, two interconnected single ended primary inductor converter (SEPIC) and Flyback converters, and two additional auxiliary controlled switches. These extra switches facilitate switching between SEPIC, Flyback, or a combination of both. The paper ex-tensively discusses the operational modes using mathematical equations, deriving specific duty cycles for each switch based on the circuit parameters. This hybrid converter aims to decrease total harmonic distortion (THD) in the line current. The findings exhibit a THD of approximately 14.51%, showcasing a 3% reduction compared to prior hybrid converters, thereby enhancing the power factor of the line current. Furthermore, at rated load conditions, the proposed converter achieves 90% efficiency. To validate the proposed hybrid converter’s functionality, a 4.5 kW converter is simulated and performed using MATLAB/Simulink after configuring the appropriate passive parameters
Accurate segmentation of fruit based on deep learning
Get PDFIn the last few years, deep learning has exhibited its efficacy and capacity in the field of computer vision owing to its exceptional precision and widespread acceptance. The primary objective of this study is to investigate an improved approach for segmentation in the context of various fruit categories. Despite the utilization of deep learning, the current segmentation techniques for various fruit items exhibit subpar performance. The proposed enhanced multiple fruit segmentation algorithm has the following main steps: 1) modifying the size of the filter, 2) the process of optimizing the ResNet-101 block involves selecting the most suitable count of repetitions. The multiple fruit dataset is split 80% in the training stage and 20% in the testing stage. These images were utilized to train a deep learning (DL) based algorithm, which aims to identify multiple fruit items within images accurately. The proposed algorithm has a lower training time compared to the other algorithms. The thresholds exhibit greater values compared to the thresholds of state-of-the-art algorithms
Implementation of a secure system for calculating and supervising the energy consumption of electrical equipment
Get PDFWith the advent of smart grids and the growing challenges associated with the production and consumption of electrical energy, it is crucial to deploy reliable systems to monitor production and consumption, as well as to improve energy efficiency. To ensure optimal decision-making in energy management and control systems, it is essential to have both efficient measurement systems for data collection and acquisition and secure information exchange. These elements are fundamental to ensuring the smooth operation of energy systems and enabling precise supervision of energy flows, thus contributing to more efficient use of available electrical resources. This article focuses on the implementation of a complete electrical energy calculation and management system for energy consumers. To achieve this, devices such as integrated digital control units and current and voltage sensors are used. The system architecture guarantees precise measurement and calculation of electrical energy and other important parameters, such as power factor in the case of inductive and capacitive loads, which have an effect on reactive energy. The data collected is stored in a secure database
Analysis and evaluation about the dimmable light affect positioning-based MISO visible light communication
Get PDFVisible light communication (VLC) is a new on-trend communication technology which offers high-speed data rate, great deployment potential in indoor enviroment. In VLC scenario, the positioning based on visible light communication (VLCP) has become one of interesting application of researchers. Most of existing proposed VLCP algorithms focused on mathematical analysis of multi-dimensional perspective based on the received signal strength (RSS) to enhance the accuracy without the consideration of dimming. However, regarding to physical characteristics of VLC devices and requirement of illumination, the light is increasingly dimmable along the time which leads to decrease transmitted optical power of LED as well as RSS received at the photodetector (PD)). Inspired by the above-mentioned constraints, this paper proposed the mathematical model to analyses the effect of dimming capability on the state-of-art RSS based positioning algorithms. Evaluation of the proposed model based on the metrics of RSS and position error (PE) is conducted on Matlab
Fuzzy based energy efficient cluster head selection with balanced clusters formation in wireless sensor networks
Get PDFThe importance of energy conservation presents a considerable challenge in wireless sensor networks (WSNs), where the sensor nodes (SNs) that constitute the network depend on battery power. Recharging the batteries of SNs in the field is challenging. The clustering technique is a commonly employed method for attaining energy efficiency. In this article, we are proposing a fuzzy-based energy efficient cluster head (CH) selection with the balanced cluster formation (FEECH-BCF) technique. It is a hybrid of the k-means algorithm, low energy adaptive clustering hierarchy- uniform size cluster (LEACH-USC) technique, and fuzzy logic technique. To create the clusters, the k-means approach is employed. The idea of LEACH-USC is used for load balancing to produce clusters with uniform size by assigning member nodes (MNs) from larger clusters to smaller clusters. Optimized CHs are selected using fuzzy based CH selection technique. The k-means algorithm is simple and quick to set up, assigning the membership of SNs to the next best cluster based on centroid locations of clusters reduces intra-cluster distance among clusters, and with the help of fuzzy logic, optimized CHs will be selected. The proposed algorithm performs exceptionally well in attaining uniform energy consumption amongst clusters and extends the network’s lifetime to a greater extent
Optimization of hybrid PV-wind systems with MPPT and fuzzy logic-based control
Get PDFThe growing demand for sustainable and reliable energy solutions has driven the development of hybrid renewable energy systems (HRES) that combine multiple energy sources. This research explores the integration of solar energy and wind energy systems, utilizing permanent magnet synchronous generators (PMSG) for wind energy conversion. PMSGs are gaining popularity due to their high efficiency and ability to operate effectively in variable-speed wind conditions, making them ideal for hybrid systems. The study focuses on optimizing the energy extraction from both PV and wind systems using maximum power point tracking (MPPT) boost converters. The control for the MPPT boost converters is based on fuzzy logic (FL), a method that offers flexibility and adaptability in managing the non-linear and dynamic characteristics of renewable energy sources. A hybrid system consisting of PV, wind energy, and a battery storage system connected to a DC bus is simulated using MATLAB Simulink. The model demonstrates the effectiveness of integrating PV and wind energy with MPPT-controlled boost converters and fuzzy logic control, ensuring optimal energy utilization, stable system performance, and efficient energy storage. This research underscores the potential of hybrid renewable energy systems, showcasing how advanced control strategies can significantly improve the efficiency and reliability of energy generation and storage solutions
Dynamic attendance system using face recognition via machine learning models
Get PDFTraditional methods to handle attendance have been implemented in the schools in the past and most of them are discouraging as they require that the institutions implement the use of paper and pen to get the results. To enhancing effectiveness and safeguarding, this paper presents a face recognition attendance system that mechanizes the usual attendance taking process. Using best practices in facial recognition, the system captures images of students’ faces, stores them, feeds them into a recognition model, and uses real-time facial recognition to mark attendance. This means that the system enjoys data encryption and password protected access that ensures data is safe. In the proposed system, the OpenCV face recognition libraries combined with machine learning algorithms for better face recognition ability with better efficiency. The results confirm that the system provides a reliable approach to handling attendance and it may debut in various contexts
UniMSE: a unified approach for multimodal sentiment analysis leveraging the CMU-MOSI Dataset
Get PDFThis paper explores multimodal sentiment analysis using the CMU-MOSI dataset to enhance emotion detection through a unified approach called UniMSE. Traditional sentiment analysis, often reliant on single modalities such as text, faces limitations in capturing complex emotional nuances. UniMSE overcomes these challenges by integrating text, audio, and visual cues, significantly improving sentiment classification accuracy. The study reviews key datasets and compares leading models, showcasing the strengths of multimodal approaches. UniMSE leverages task formalization, pre-trained modality fusion, and multimodal contrastive learning, achieving superior performance on widely used benchmarks like MOSI and MOSEI. Additionally, the paper addresses the difficulties in effectively fusing diverse modalities and interpreting non-verbal signals, including sarcasm and tone. Future research directions are proposed to further advance multimodal sentiment analysis, with potential applications in areas like social media monitoring and mental health assessment. This work highlights UniMSE's contribution to developing more empathetic artificial intelligence (AI) systems capable of understanding complex emotional expressions
An efficient hardware implementation of number theoretic transform for CRYSTALS-Kyber post-quantum cryptography
Get PDFCRYSTALS-Kyber was chosen to be the standardized key encapsulation mechanisms (KEMs) out of the finalists in the third round of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization program. Since the number theoretic transform (NTT) was used to reduce the computational complexity of polynomial multiplication, it has always been a crucial arithmetic component in CRYSTALS-Kyber design. In this paper, a simple and efficient architecture for NTT is presented where we easily archived the functionality of polynomial multiplication with efficient computation time. Only 857 Look-Up Tables and 744 flip-flops were utilized in our NTT design, which consisted of two processing elements (PEs) and two butterfly cores within each PE