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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