ITEGAM-JETIA

Speed Control of PMSM drive using Model Predictive Control Based Field Oriented Control

2025-04-01T21:29:51-03:00

This paper examines the enhancement of Permanent Magnet Synchronous Motor (PMSM) drives through the integration of Field-Oriented Control (FOC) with Model Predictive Control (MPC). The study aims to achieve high precision and dynamic response for PMSM drives under diverse operating conditions. The theoretical framework combines FOC and MPC principles, utilizing MPC's predictive capabilities to optimize d-q current references in real-time. The methodology encompasses the design and implementation of an MPC technique integrated with FOC, with key objectives including the minimization of torque ripples, the maintenance of system stability through robust control loops, and the optimization of PMSM drive performance across a wide speed range. The results indicate significant improvements in torque ripple reduction, dynamic response, and disturbance rejection, demonstrating the robustness and adaptability of the proposed control system. This approach effectively addresses key challenges and signifies advancements over traditional control methods, contributing to the field of electric drive control systems.

Optimal sizing of a hybrid microgrid system for a rural area of Algeria

2025-04-01T21:29:51-03:00

Renewable energy systems have replaced systems that use fossil fuels in many applications in different regions of the world. This is seen in the increasing use of solar and wind energy as the two most important sources for producing environment-friendly and economically convenient electrical energy. The fluctuating and unstable nature of renewable energy sources makes this type of energy complex to exploit, and related research has therefore mainly focused on Control and optimization. This work proposes an optimized configuration of two hybrid systems designed for a microgrid network with the aim to improve the power supply in isolated areas and provide a low cost, more reliable, and sustainable source of electricity for rural communities that may have limited access to traditional power grids. These hybrid setups consist of an initial system that caters for 10 houses which is then extended to serve 20 houses. Both setups utilize solar and wind energy sources, energy storage batteries, and a diesel generator. Real data collected in the Biskra region in the southeast of Algeria, is used. Particle Swarm Optimization algorithm is applied to achieve the optimal size of the hybrid system components through the weighted sum multi-objective approach, whereby three factors, namely, Cost of Electricity, Loss of Power Supply Probability, and Dummy Excess are combined into one objective function. Results of simulation show that the proposed approach achieves highly satisfactory values for the electricity prices in the 10- house and 20-house scenarios, with estimates of 0.15829 $/Kwh and 0.42112 $/Kwh, respectively.

Performance enhancement of wind energy conversion systems using PMSG with backstepping and ANN-based MPPT control

2025-04-01T21:29:50-03:00

This paper investigates the performance enhancement of wind energy conversion systems (WECS) using a Permanent Magnet Synchronous Generator (PMSG) specifically designed for standalone, fixed-pitch, variable-speed wind turbines. The study focuses on controlling the PMSG using Backstepping control which enables Maximum Power Point Tracking (MPPT) using the Tip Speed Ratio (TSR) method. Initially, a Proportional-Integral (PI) controller was implemented for regulating the generator speed. However, this approach encountered significant limitations, particularly in managing speed overshoot and responsiveness under fluctuating wind conditions. To address these issues, a neural network controller was introduced as a replacement for the conventional PI controller. This neural network controller provides an adaptive control mechanism capable of dynamically adjusting to change, thereby eliminating overshoot and greatly enhancing response speed and overall system stability. This work provides a reliable and efficient control solution where was the proposed control strategy was rigorously evaluated through numerical simulations in Matlab/Simulink, which confirmed its ability to stabilize the system and achieving both steady-state and dynamic optimization of wind energy conversion systems.

USE OF TARO BOGOR ( Colocasia esculenta ( l.) Schott ) AS A GROWTH MEDIUM FOR Aspergillus flavus

2025-04-01T21:29:50-03:00

Fungi are one type of microbe that plays an important role in human life, there are many groups of fungi that can be found in everyday life, one type of fungus that is pathogenic and produces aflatoxins, namely fungi with Aspergillus flavus species. Bogor taro has the potential to be used as a raw material because it has a carbohydrate content of 23.7% as an energy source, while potatoes have a total carbohydrate of 19.10%. So it can be known that the amount of carbohydrates in taro bogor is more than potatoes. Based on this background, researchers are interested in conducting research related to the use of taro bogor ( Colocasia esculenta ( L.) Schott ) as a growth medium for the fungus Aspergillus flavus. This type of research is experimental with Posttest Only Control Group Design research design, in this study the control group in the form of Aspergillus flavus planted on PDA medium while in the experimental group in the form of Aspergillus flavus planted on taro medium at a concentration of 2%. 4% and 6%. Conclusion based on the results of the paired T-test test, the p-value of taro medium is 2% and 4% less than 0.05 which means there is a significant difference with PDA medium, while in taro medium 6% the p-value of > 0.05 which can be concluded that there is no significant difference between taro medium 6% and PDA medium.

Modelling corona discharge threshold in composite dielectric properties of eggshell composite insulators: A VOLTAGE-BASED STUDY

2025-04-01T21:29:49-03:00

Corona and flashover threshold voltages are affected by the characteristics of insulator materials, such as chemical composition and thickness, as well as environmental conditions, including humidity and temperature. Eggshell powder is produced from the impact of crushed eggshells; this powder will be used as a substitute and filler (cement) used in the pavement mixture on solid insulators. This study aims to understand the difference in the response of flashover and corona phenomena to increased voltage in composite insulators. Flashovers tend to occur at low voltages and decrease drastically at high voltages, while coronas rarely appear at low voltages but increase and stabilize at high voltages. Flashovers are rare at voltages over 20 kV, but corona is still possible. This suggests that the design of insulators or conductors for high voltages needs to pay attention to the corona effect which can lead to material degradation over time, while the risk of flashover is lower. In this study, the voltage threshold for the emergence of corona is about 20 kV, where the electric field is strong enough to trigger an electrical discharge around the conductor. After crossing this limit, the corona remains at high voltage. Therefore, the design of the material or insulator must consider the constant presence of corona at high voltages to prevent material damage due to the discharge of electrical energy around the conductor.

Hopf bifurcation point identification in a power system with various static load models

2025-04-01T21:29:49-03:00

This paper covers the impact of voltage dependent load models on the small signal stability in Western States Coordinating Council (WSCC) system having Automatic Voltage Regulator (AVR) with increased generation and load scenarios. Earlier work on the same system using constant power type of load representation for locating the Hopf bifurcation point was done by the authors of this manuscript. This paper deals with the extension of the previous work to include various voltage dependent small signal load models such as Constant Current (CC), Constant Impedance (CZ), Industrial (IND) and large scale Electric Vehicle (EV) for evaluating damping ratios and sketching the locus of critical modes at stressed loadings by defining six cases. A thorough comparison of damping ratios of several complex modes obtained after including various voltage dependent load models in the linearization programmes with the constant power model presented in earlier work at the nominal loading is done. It is concluded that by including large scale EV load model at load buses, highest damping ratio of exciter mode corresponding to first generator of WSCC system was obtained when compared to remaining load models.

Hybrid FFT-ARMA-Burg Modeling and LSTM-Enhanced BBO Optimization for Fault Diagnosis in Induction Motors

2025-04-01T21:29:48-03:00

Induction motors (IM) are crucial in industrial systems, and fault diagnosis reliably and effectively is of importance for maintaining efficiency. The detection of a broken rotor bar(RBB) is one of the most challenging tasks in condition monitoring due to the complexity of fault features in motor current signals. This plans overcome by developing a hybrid diagnostic framework to enhance fault detection accuracy. The proposed approach fuses a hybrid spectral analysis technique that integrates the Fast Fourier Transform with an autoregressive moving average model estimated using Burg's method. This hybrid of FFT-ARMA-Burg enhances PSD representation. We employ biogeography-based optimization to optimally tune the parameters of the ARMA-Burg model for a better representation of fault-specific features. Further, this paper proposes an LSTM neural network that refines BBO-optimized parameters to improve fault frequency sensitivity. Experimental verification will demonstrate that the hybrid FFT-ARMA-Burg framework, combined with LSTM-enhanced BBO optimization, outperforms traditional motor current signature analysis (MCSA) and standalone ARMA-based methods in detecting broken rotor bars in squirrel cage induction motors. These findings confirm that the proposed methodology enhances broken rotor bar detection and supports predictive maintenance for improved reliability and efficiency in induction motors.

Enhancing sentiment analysis in tourism reviews: A comparative study of algorithms in ASPECT-BASED SENTIMENT ANALYSIS and EMOTION DETECTION

2025-04-01T21:29:48-03:00

Information technology now enables utilizing online review data to support the Sustainable Development Goals (SDGs). However, traditional sentiment analysis often cannot capture the complexity of sentiment. This research aims to combine Aspect-Based Sentiment Analysis (ABSA) and emotion detection for a more in-depth analysis of tourism reviews in Palangka Raya City and compare the performance of various algorithms. Review data was taken from Google Maps and analyzed using BoW, LDA, NRC Emotion Lexicon, machine learning, and deep learning algorithms such as Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Decision Tree (DT), and BERT. Most of the reviews are positive, with the dominance of the emotions of anticipation and joy. The combination of cross-validation with the best parameters from GridSearchCV resulted in the most significant increase in model accuracy. The SVM model performed better than other machine learning and deep learning algorithms, with accuracy and F1-score reaching 99.86%. The combination of ABSA and emotion detection improves the understanding of sentiment and emotion to support strategic decisions in tourism.

Navigation system and obstacle avoidance for mobile robot using Type-2 fuzzy logic in uncertain environments

2025-04-01T21:29:47-03:00

This paper introduces a reactive navigation strategy for wheeled mobile robots, utilizing type-2 fuzzy logic to manage uncertainty in dynamic environments. The approach incorporates two distinct type-2 fuzzy logic controllers, each tailored to address specific challenges in navigation. The first controller focuses on steering the robot toward its target by continuously adjusting its path in response to changing conditions. The second controller specializes in obstacle avoidance, enabling the robot to detect and maneuver around obstacles it encounters during its journey.

To evaluate the performance of the system, numerical simulations are carried out across various scenarios, including dynamic and cluttered environments, to demonstrate its robustness. Additionally, the results of the type-2 fuzzy logic approach are compared with conventional navigation techniques, such as rule-based or model-based methods. The comparison underscores the system’s greater adaptability and resilience. The study concludes that type-2 fuzzy logic provides an effective and flexible solution, significantly improving both path planning and real-time decision-making in unpredictable and complex environments.

Designing a smart home model using EasyVR and Arduino for voice control of devices

2025-04-01T21:29:47-03:00

This paper presents the design and implementation of a smart home model using easyVR and Arduino to voice control home appliances, without relying on traditional sensors. The system leverages easyVR, a voice recognition module, to process spoken commands, which are then interpreted by Arduino to control various smart devices, such as lights, TVs, and air conditioners. The proposed system aims to provide an affordable and efficient solution for home automation, offering a user-friendly, hands-free interface for users. The paper identifies the hardware and software components of the system, including the integration of easyVR with Arduino, and addresses the challenges of implementing accurate voice recognition in a real-world environment. A prototype is developed to demonstrate the functionality and effectiveness of the voice control system. The results show that the system responds reliably to voice commands, providing a seamless smart home experience. This paper demonstrates the potential of easyVR and Arduino in creating scalable and cost-effective voice-controlled smart homes.

A Comparative Analysis of Fast Charging Performance and Battery Life Against Charging Current Variations

2025-04-01T21:29:45-03:00

Meningkatnya permintaan untuk baterai pengisian cepat menghadirkan tantangan terkait kecepatan pengisian dan masa pakai baterai. Studi ini membandingkan kinerja baterai lithium-ion dan solid-state di bawah berbagai arus pengisian. Pada arus pengisian 50 A, baterai lithium-ion membutuhkan sekitar 140 detik untuk mengisi penuh, sedangkan baterai solid-state mencapai hal yang sama dalam waktu sekitar 130 detik. Namun, pada arus yang lebih tinggi (300 A), baterai lithium-ion secara signifikan mengurangi waktu pengisian menjadi 20 detik, sedangkan baterai solid-state mengisi daya dalam 18 detik. Meskipun demikian, pengisian arus tinggi mempercepat degradasi baterai. Baterai lithium-ion, misalnya, memiliki masa pakai sekitar 1.200 siklus pada 50 A, yang menurun secara drastis menjadi mendekati 0 siklus pada 300 A. Sebaliknya, baterai solid-state mempertahankan masa pakai yang lebih tinggi, berkurang dari 1.000 siklus pada 50 A menjadi sekitar 100 siklus pada 300 A. Baterai solid-state menunjukkan kinerja yang unggul, terutama dalam kondisi arus tinggi, dengan 600 siklus pada 150 A, dibandingkan dengan hanya 200 siklus untuk lithium-ion. Hasilnya menyoroti trade-off antara kecepatan pengisian dan masa pakai baterai, dengan pengisian daya yang lebih cepat dicapai dengan mengorbankan umur panjang baterai, terutama untuk lithium-ion. Teknologi solid-state memberikan solusi yang lebih seimbang, menawarkan waktu pengisian yang lebih cepat dan umur panjang yang lebih baik, sehingga cocok untuk aplikasi berdaya tinggi.

Quantitative analysis of subsurface structure cracking using pulsed eddy current nondestructive testing

2025-04-01T21:29:46-03:00

This paper presents a three-dimensional finite element method for the nondestructive evaluation of forward problems utilizing the pulsed eddy current technique. The method visualizes and maps the distribution of responses resulting from the interaction between eddy currents and defects, facilitating defect characterization. The study elaborates on the defect characterization process using the pulsed eddy current technique, which encompasses both numerical and experimental analyses. Initially, the variation in pulse width of the pulsed eddy current technique is discussed, along with its effectiveness. Subsequently, the investigation into the application of pulsed eddy current testing for defects is conducted through the mapping of magnetic field distributions, implemented via time-stepping three-dimensional finite element modelling, with features extracted from the mapping for the purpose of defect characterization.

Sub-Module Voltage Estimation of Modular Multilevel Converter in PV Applications Using a Sliding Mode Observer

2025-04-01T21:29:46-03:00

This paper presents a Sliding Mode Observer (SMO) for estimating sub-module (SM) voltages in Modular Multilevel Converters (MMCs) used in photovoltaic (PV) applications. MMCs are widely favored in medium- and high-voltage applications due to their modularity and scalability. However, traditional voltage sensing methods require numerous sensors, increasing system complexity, cost, and susceptibility to sensor failures. The proposed SMO offers a robust alternative, providing accurate voltage estimation despite parameter variations and external disturbances. The design and implementation of the SMO, along with its integration into the MMC control strategy, are demonstrated. The observer performance is validated through simulations and Matlab results, which demonstrate effective voltage estimation across different operating conditions. This method improves SM capacitor voltage balancing, enhances the reliable operation of MMCs in PV applications, and lowers costs.

The Incorporating ANFIS-based current regulation into a multi-active full bridge converter for reducing current ripple

2025-04-01T21:29:45-03:00

A multi-active full bridge converter is a type of DC-DC converter typically used in high-power applications like renewable energy systems, electric vehicles, or uninterruptible power supplies. Four switches (often MOSFETs or IGBTs) grouped in an H-bridge configuration make up the full bridge topology. The term "multi-active" implies that the power conversion involves a number of active components, which may indicate complicated switching management for improved efficiency and regulation. Changing the phase shift between two switch pairs is how phase-shifted full-bridge converters are controlled. Without altering the switching frequency, the output voltage can be controlled by adjusting the phase difference between the bridge's two legs (S1-S4 and S2-S3). To lower switching losses, soft switching strategies like ZVS or ZCS might be used.

improving overall efficiency. In this approach, the current through the switches is monitored and controlled to improve transient response and stability. Typically implemented in combination with voltage mode control, it ensures better regulation, especially in systems with fast load changes. The secondary side full bridge switches are controlled by current feedback phase delay controller which restricts the battery charge current. Each EV battery has its own charge current limit which is set in the current controller for battery protection. The controlled charge current improves the charge efficiency of the EV battery increasing the reliability of the cells and the overall circuit. The charge control which is a conventional PI controller is further updated with ANFIS for better stability in the EV battery charging current. A comparative analysis with PI and ANFIS controllers is proposed and the results are generated with same rating of EV batteries and system parameters.

Potential Adoption of Blockchain in Food Cold Supply Chain: A Bibliometric Study and Future Research Agenda

2025-04-01T21:29:44-03:00

The food cold chain represents a crucial aspect of the highly sensitive food industry. Insufficient supervision can give rise to a range of adverse consequences, including contamination, fraud, and food waste. In line with technological advancement, the implementation of cold chains is becoming increasingly integrated with digital solutions, with blockchain technology representing a notable example. However, some bibliometric analyses have not focused on the cold supply chain, limiting their scope to the supply chain in general. Consequently, further studies are required to gain a more comprehensive understanding of this complex and evolving field. The objective of this analysis is to provide a bibliometric examination of the potential implementation of blockchain technology in the context of food cold chains. This will facilitate the identification of the most recent developments, prevailing trends, and research gaps that require further investigation. This research uses the combination of PRISMA and bibliometric analysis method through Biblioshiny and Vosviewer using 201 scientific articles. The results show that this topic has an annual growth of 96.77%, and there are 3 interesting research clusters, namely 1) blockchain technology transformation in improving traceability and reducing food waste, 2) blockchain technology in improving the competitiveness of cold supply chain companies, and 3) blockchain that can improve food safety, leading to increased consumer confidence. Based on the major themes obtained and strategic diagram, there are various future research opportunities, including capability freight transportation, deteoritation, food contamination, storage and transportation, carbon emissions, competitiveness, delphi analysis, agricultural products and sales.