IJEER Vol no. 13, Issue 2


Performance Improvement of PMSM Using PID and GA-PID Controllers

A permanent magnet synchronous motor (PMSM) is widely used in AC servo drives because of its high-power density and high torque for industrial applications, with a wide range of applications. The Permanent Magnet Synchronous Motor is modeled, and simulation is used in MATLAB's Simulink. After representing the motor mathematically with the transfer function according to characteristics suitable for applications similar to the proposed characteristics. This paper proposes using PID to improve the performance of PMSM. Then, the genetic algorithm, an optimization method, is used to adjust the P, I, and D parameters. Simulation tests are conducted for an open and closed system circuit without control and with control. The outcomes are contrasted with conventional PID controller tuning by genetic algorithm.

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Extending the Hopf Bifurcation Limit Using Power System Stabilizer in a Two Area Power System Having Huge Industrial Loads

he phenomenon of Hopf bifurcation is observed in several engineering and non-engineering domains which use both linear and nonlinear dynamic models. One parameter and two parameter variations of set of Differential Algebraic Equations of Kundur two area system was explored in this manuscript assisted with eight different cases to have detailed insight into Hopf bifurcation by modelling the load buses with industrial loads.

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Adaptive Predictive Control with Non-Integral Voltage Monitoring for Enhanced Shunt Hybrid Active Power Filters

This paper presents a hybrid control strategy that integrates Adaptive Predictive Deadbeat Current Control with a Non-Integral AC Capacitor Voltage Monitoring Method to enhance the performance and reliability of Shunt Hybrid Active Power Filters (SHAPFs). The proposed approach combines the precision and fast dynamic response of predictive deadbeat control for harmonic compensation with a novel non-integral method for calculating the AC capacitor voltage, mitigating the risk of overvoltage without the accumulation of errors typical in integral-based methods. Simulation results demonstrate significant improvements over traditional methods. The proposed approach reduces Total Harmonic Distortion (THD) from 21% to 2.8%, achieving a improvement over conventional PI-based controllers.

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Data Mining in Power System Fault Identification using Artificial Intelligence

This paper presents a power system fault identification method by simultaneously applying the Kendall and Spearman correlation coefficients for feature selection, combined with an Artificial Neural Network (ANN) to enhance accuracy and optimize training time. Experimental results indicate that Kendall demonstrates superior performance in handling nonlinear data and mitigating the impact of outliers, leading to more optimal fault identification outcomes. Backpropagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models are trained on datasets after feature selection using both correlation coefficients.

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Performance Evaluation of Hybrid Chaotic and Permutation Schemes for Image Transmission Based MC-CDMA

The demand for more reliable and efficient multimedia data transfer through wireless communications channels is escalating. However, multimedia data, such as images, suffers significantly from wireless channel effects, including interference, fading, and burst errors. The Multi-Carrier Code Division Multiple Access (MC-CDMA) technology is regarded as the most efficient method for data transfer across wireless networks, supporting multiple users simultaneously without requiring an expansion of the frequency band. This paper employs permutation and hybrid chaotic techniques to demonstrate image transmission performance over the MC-CDMA network.

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Optimizing Configurable Logic Blocks with Advanced Error-Resilient Circuits for Low-Power FPGA Systems

This paper aims at enhancing configurable logic blocks (CLBs) in FPGA systems through incorporating more complex error-tolerant circuits and power control strategies. The architecture of the Presented FPGA considered in this study has been designed using MATLAB simulation and is tailored for low power consumption and high reliability. Power management is another feature implemented in the system through Dynamic Voltage Scaling (DVS) to improve electrical power usage essentially by 20%-25% at low load”. The fault tolerance is implemented through incorporating ECC and TMR into CLBs to render the system capable of tolerating with faults and still work efficiently.

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Performance Driven Outlier Detection in Health-Care Data: A Hybrid Approach Using Dual-Feature Optimization and Segmentation Techniques

The healthcare sector is a domain where the implementation of human-centered design approaches and concepts can significantly impact well-being and patient care. Delivering superior medical care necessitates a profound comprehension of an individual's desires, encounters, and interests. This study examined the quantitative evaluation and utilization of MRI scans for preoperative conditions of the brain, lungs, and heart. However, identifying these intricate compositions is a formidable challenge. Traditional diagnostic methods are laborious and rely heavily on the clinical expertise of radiologists. This research proposes a non-invasive automatic diagnosis system for diseases utilizing hybrid deep learning approaches, specifically LSTM & PSO (Long Short-Term Memory & Particle Swarm Optimization), to improve the efficiency of outlier detection.

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Application of LSTM and GRU Neural Networks in Forecasting the Power Output of Wind Power Plant

This paper proposes the application of artificial intelligence to forecast the generation capacity of wind power plants by processing data through noise reduction and filtering. It subsequently employs Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for training, testing, and evaluation. Processing the initial data will help minimize noise and reduce the data space. The study focuses on preprocessing methods and selecting the appropriate neural network between LSTM and GRU. The initial data processing will assess the similarity through the Spearman rank correlation coefficient. The data used in the paper is taken from local wind turbines.

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Numerical Simulation on Charge Transport in Polyethylene with Field-Dependent Parameters Under DC Electric Field

During the past few years, the use of HVDC cables has increased exponentially. However, the accumulation of space charges within insulating materials remains a major challenge. Understanding the mechanisms governing this phenomenon is key to improving HVDC performance. This goal is often achieved through numerical simulations. Therefore, it is imperative that they are performed efficiently. In this work, a bipolar charge transport (BCT) model is used to offer a physical description of space charge behavior in low-density polyethylene (LDPE) under a high DC electric field. This model includes injection, migration, trapping, dettraping and recombination charges with parameters dependent on the electric field such as mobility, trapping, and recombination.

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Analysis of Copper and Iron Loss Interactions in a 15 kW Three-Phase Induction Motor under Variable Operating Conditions

This research presents an innovative contribution to the field of induction motor efficiency optimization by analyzing the complex interactions between copper and iron losses in a 15 kW three-phase induction motor under variable operating conditions (10–60 Hz).

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An Improved UFLD-V2 Lane Line Recognition Method

Lane line recognition remains a crucial component of autonomous driving, particularly under complex scenarios involving illumination changes and occlusions. This paper presents a structurally efficient and robust improvement of the UFLD-V2 architecture, designed for real-time and reliable lane detection. The proposed method integrates three lightweight yet complementary components: (1) Res2Net, replacing the original ResNet backbone, enhances multi-scale feature extraction and inference efficiency through reparameterization; (2) an Efficient Multi-scale Attention (EMA) module captures fine-grained contextual details across varying scene complexities; and (3) the Simple Attention Module (SimAM) is applied in the segmentation head to suppress background noise and improve localization accuracy.

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Real-Time Traffic Light Optimization Using Yolov9 and Length-Based Metrics

The Indian traffic control system faces lots of difficulties due to the increasing volume of vehicles, ineffective systems for traffic administration during peak hours, and the frequent need for manual intervention due to the inadequate performance of traffic signals in managing heavy traffic flow. Traditional traffic lights in India have defined timings for each lane, which frequently cause longer traffic jams in lanes with more traffic. This study presents an intelligent traffic control system that incorporates the YOLOv9 model for real-time traffic length prediction and intelligently allocates green, red, and orange signal timings. YOLOv9 builds a bounding box that allows it to compute vehicle density precisely by enclosing the initial and final cars in every frame.

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A Lightweight CNN Architecture for Efficient Brain Tumor Detection in MRI Scans

The intricate morphology of brain tumors poses significant diagnostic challenges in MRI interpretation. While AI-driven systems offer potential for automation, balancing accuracy with computational efficiency remains critical for clinical adoption. This work introduces a lightweight convolutional neural network optimized for brain tumor detection and classification in MRI scans. The architecture’s design emphasizes a systematic exploration of layer-ordering strategies, with experiments revealing that batch normalization in post-activation mode (Post-BN) outperforms Pre-BN in training stability and classification accuracy.

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Tunable Triple-Notched Ultra-Wideband Bandpass Filter for Efficient In-Band and Out-of-Band Interference Mitigation

This study analyzes the growing need for effective in-band and out-of-band interference mitigation in ultra-wideband (UWB) communication systems. We present a novel microstrip bandpass filter (BPF) with changeable triple-notched bands that preserves a large passband and a higher stopband. The filter comprises a multimode resonator (MMR) architecture that incorporates a hollow T-shaped structure that generates two transmission zeros at the passband boundaries, thereby boosting selectivity.

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Design of Fractal Antenna For S-Band and X-Band Applications

A fractal antenna that can be used for S-band and X-band applications is proposed in this paper. The antenna is able to be easily integrated into naval radar systems. The antenna is made up of fractal structures that are cross-shaped and organized in stairwell-like repetitions. It is a two-port antenna that receives its feed via coaxial cable. Meta materials are integrated into the design to achieve bidirectional gain and reduce mutual coupling.

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Performance Analysis of Tera Hertz Frequencies on Intelligent Reflecting Surfaces for 6G Communications

The rapid advancements toward 6G networks have powered interest in the terahertz (THz) frequency band due to its potential for delivering ultra-high data rates. It provides massive connectivity. However, THz communication faces significant challenges. It includes high path loss, molecular absorption and blockage sensitivity. These are severely degrading signal quality over distance. Intelligent reflecting surfaces (IRS) emerged as a promising solution to address these issues. IRSs reflect and direct signals to desired locations. This improves communication quality. It does not require extra power sources.

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Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery

Reliable and accurate fault diagnosis in rotating machinery is vital for minimizing unplanned downtime, reducing maintenance costs, and ensuring operational safety in industrial environments. Traditional diagnostic approaches depend heavily on manual feature extraction from vibration signals, which can be time-consuming, expertise-dependent, and prone to missing subtle fault patterns. This study presents a novel hybrid framework—IDL-OFSVM—that combines Intelligent Deep Learning (IDL) with an Optimized Fuzzy Support Vector Machine (OFSVM) for automated fault classification. Vibration signals are first transformed using the Continuous Wavelet Transform (CWT), and deep features are extracted via the lightweight MobileNet architecture.

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Allocate Compatible Locations of TCSC in Baghdad City: A Case Study

Power systems are generally required to operate near their maximum capacity. Thus, there is an increased focus on improving real power system capacities through the installation of novel devices, including the Flexible AC Transmission Systems (FACTs). This paper discusses the optimal incorporation of thyristor-controlled series capacitor (TCSC) in the 400 kV Baghdad grid of the Iraqi network as a suggested method to control the power transfer of a transmission line (TL) and suppress Sub-Synchronous Resonance (SSR).

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Analysis of Constant Losses of Three-Phase Squirrel Cage Induction Motor with Different Types of Eccentricity Under No Load Operating Condition

This research investigates the core loss of a 5 HP three-phase squirrel cage induction motor across different levels of three types of eccentricities (static, dynamic, and mixed) utilizing Ansys Maxwell software. The simulation outcomes for core losses are compared with analytical core loss values. No load and loss separation tests conducted under healthy conditions and at 12% static, 12% dynamic, and 12% mixed eccentricities to determine constant loss and isolate the core loss from constant loss.

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IntelligentGuard: Smart Doorbell with Deep Learning for Secure User Recognition and Instant Notifications

In the modern world, daily activities are heavily reliant on the Internet. This study aims to provide users with a simple, personalized technology that effectively manages visitor interactions. The primary objectives are to operate the doorbell intelligently and notify users about visitors by sending a notification with an image of an unknown visitor. This system introduces a low-cost Internet of Things (IoT) smart doorbell designed to enhance home security, utilizing a Raspberry Pi and a camera sensor. Camera sensor is used to capture images in front of the doorbell, which are then processed by the Raspberry Pi and sent to the server.

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Optimizing MPPT Extraction in Hybrid Energy Systems Using an Adaptive PSO Topology

In the modern world, daily activities are heavily reliant on the Internet. This study aims to provide users with a simple, personalized technology that effectively manages visitor interactions. The primary objectives are to operate the doorbell intelligently and notify users about visitors by sending a notification with an image of an unknown visitor. This system introduces a low-cost Internet of Things (IoT) smart doorbell designed to enhance home security, utilizing a Raspberry Pi and a camera sensor. Camera sensor is used to capture images in front of the doorbell, which are then processed by the Raspberry Pi and sent to the server.

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Designing Smart Perturb and Observe MPPT Controller for 150W Off-Grid PV System

Studying the introduction design of a stand-alone solar photovoltaic (PV) system with a disturbance-and-observation (P&O)-based algorithm for maximum power point tracking (MPPT) is an important topic for improving the performance of solar energy systems. The efficient energy system is designed by using solar photovoltaic panels consisting of a number of cells, using P and O algorithms for effective tracking of maximum power, and performing comparative analysis using the traditional model without the MPPT algorithm.

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Design Patch Antenna at Wi-Fi Applications for Detection Breast Cancer Tumors

The development in the science of communications technology in the medical fields has led to the use of patch antennas for applications in biomedical applications and for the Wi-Fi band, which ensures reliable results for detecting tumors. Antenna is deliberate using Rojers R03203 with 0.75 mm for thickness of the substrate and 3.02 for the permittivity. The actual size of this antenna is (36⋅58 ⋅0.75) mm.

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