International Journal of Electrical Engineering and Applied Sciences (IJEEAS)

ASSESSMENT OF OVERCURRENT PROTECTION OF TNB NETWORK USING PSCAD SOFTWARE

Mohd Hendra Hairi — 2025-02-24

This research focuses on assessing IDMT overcurrent protection in the TNB network, particularly in the Pencawang Masuk Utama (PMU) at Pagoh, using PSCAD software simulations. The study aims to evaluate the effectiveness and reliability of existing protection methods, analyzing fault reactions, coordination, and fault-clearing times. The TNB network model simulates diverse fault scenarios, enabling a comprehensive examination. Proposed enhancements, such as adjusting relay settings and implementing advanced protection technologies, aim to improve system dependability. The findings aim to guide TNB in optimizing overcurrent protection and offer insights applicable to similar power utilities. Ultimately, the project contributes to the ongoing improvement of TNB's protective infrastructure, ensuring a consistent and reliable power supply for customers.

Solar-Powered Peltier Thermoelectric Water Cooling and pH Monitoring

Khasyfee Ihtifazhuddin Jasmee — 2025-02-24

This project focuses on the development of a solar-powered system for water cooling and pH monitoring using Peltier thermoelectric technology. The system aims to maintain optimal water temperature and pH levels for hydroponic applications. The system design involves the integration of solar panels, Peltier modules, and sensors to achieve the desired cooling and monitoring functions. Simulation and hardware testing were conducted to validate the system's performance. Results showed that the system effectively maintained water temperature below 29°C and pH levels between 5.5 and 6.5, demonstrating its potential for enhancing hydroponic crop yields and resource efficiency. This research highlights the feasibility of using renewable energy sources for agricultural applications and presents a significant advancement in sustainable farming technology.

Sigmoid-Function-Based Adaptive Pelican Optimization Algorithm for Global Optimization

Abdul-Fatawu Seini Yussif — 2025-02-24

This paper introduces the Sigmoid-function-based Adaptive Pelican Optimization Algorithm (MPOA), an enhanced version of the traditional Pelican Optimization Algorithm (POA) aimed at improving the POA's performance. Inspired by the hunting behavior of pelicans, the POA features two main strategies: the Exploration phase and the Exploitation phase. The Exploration phase involves searching new areas within the solution space, while the Exploitation phase focuses on refining the optimal solution space to achieve convergence. However, the Exploitation phase is inefficient, leading to slower convergence rates when striving for a global optimum. The MPOA incorporates an adaptive inertia weight mechanism that leverages the sigmoid function to balance exploration and exploitation throughout the optimization process. This adaptive approach ensures an efficient transition between searching for new solution areas and refining existing ones, thereby enhancing the overall optimization process. The algorithm was tested using a set of widely recognized standard benchmark functions to assess its performance. The results demonstrated that the MPOA significantly improved both convergence speed and solution quality compared to the original POA. Additionally, the MPOA outperformed other traditional optimization algorithms, such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), in terms of achieving better optimization results. These findings suggest that the proposed MPOA provides an efficient optimization approach, leading to faster convergence and higher-quality solutions.

Improving grid-connected tidal power systems through PWM inverters based on voltage-oriented control

Ladislas Kangaji — 2025-02-24

Pulse width modulation (PWM) rectifiers play a critical role in AC to DC power conversion for renewable energy applications. Existing systems predominantly employ basic voltage-oriented control (VOC) strategies and conventional 2-level converters, which often face challenges such as suboptimal steady-state performance, slower transient response, and elevated harmonic distortions. Moreover, these systems typically lack sophisticated control mechanisms for managing active and reactive power, thereby potentially compromising overall efficiency and stability. This proposed voltage-oriented control strategy aims to overcome these limitations by enhancing both steady-state performance and transient response through an inherent current control loop. Specifically, in the context of grid-connected tidal energy systems, the generator side utilizes field-oriented control (FOC), while the grid side employs a 3-level H-bridge voltage source converter integrated with VOC and a phase-locked loop (PLL). These advanced control strategies are designed to optimize power extraction and more effectively manage active and reactive powers. MATLAB/Simulink simulations have been conducted to validate the efficacy of the proposed controller, demonstrating its ability to ensure system stability, reduce harmonic distortions, and manage reactive power effectively. The system, featuring a 1.5 MW/C, 1.2 MW three-level inverter, and LCL filter, achieves harmonic distortion below 5%, showcasing the filter's efficiency. The comprehensive simulation results support the feasibility and effectiveness of the proposed voltage-oriented control strategy, addressing total harmonic distortion (THD), dynamic performance, and parameter sensitivity.

Integration of Artificial Neural Network in a IEEE 5 BUS System

Thomas Lionel MAKOSSO — 2025-02-24

The management and the operation of the modern grid is getting more and more complex, especially with the introduction of distributed generation. Grid stability considerations in contemporary power systems are also influenced by the integration of renewable energy, modifications in consumer behaviour, and emerging technology. The study of faults impacts in the distribution network is pertinent and important since it helps to increase the efficiency, safety, and dependability of the power system. It gives utilities the ability to react rapidly to problems, improve maintenance procedures, and prepare for the integration of new technologies—all of which are essential for providing consumers with steady and uninterrupted electricity. In the context of Artificial Neural Networks (ANNs), the Levenberg-Marquardt (LM) algorithm is an extensively utilized optimization method. It is generally used to train feedforward neural networks, especially when those networks have several layers and complicated optimization problems. Introducing a fault in a IEEE 5 bus system help to analyse the changes in the system. The data collected from that phase to phase voltage fault are computed and train in the proposed model and proves the higher efficiency and ability to detect faults or abnormal disturbances. The simulations are done using MATLAB/Simulink

REVIEW OF DIFFERENT TYPES OF NEURAL NETWORK ARCHITECTURES

Thomas Lionel MAKOSSO — 2025-02-24

Innovative technologies come with such a huge amount of data that can only computerize with fast and more complex software. As time went by, more complicated problems arose such as pattern recognition, machine learning and prediction and unfortunately the conventional computer system was unable to carry out such tasks. Which leads to intelligent computational systems such as artificial neural networks. It is developed so that artificial neurons combined together would behave like a human brain. Different layers of mathematical processing are used to provide an accurate response regarding the input. Based on their architecture, training or learning methodology, and activation function, these artificial neurons are classified. The arrangement of neurons to create layers and the connections between and within the layers make up the neural network architecture. This paper aims to provide a clear and concise understanding of several types of architecture and its applications. Five mains' architectures and their applications and gaps are presented in this paper. The different architectures are: feed-forward, Convolutional and, recurrent neural networks, Auto encoder and generational encoders and Deep reinforcement learning architecture.

Understanding the Discharge Characteristics of Lithium Iron Phosphate and Lithium Cobalt Oxide Cathode Materials

Rahifa Ranom — 2025-02-24

This paper focuses on the discharge characteristics of two cathode materials, namely Lithium Iron Phosphate (LiFePO₄) and Lithium Cobalt Oxide (LiCoO₂). The two cathode materials have different discharge characteristics and maximum concentrations. A mathematical model of lithium ion battery (LIB) for half-cell cathode based on drift-diffusion model is used to model the practical operations of LIB. This model takes into account the electrolyte equation and lithium transport equation in the electrode particles and is solved numerically using the Method of Line technique. The simulated discharge curves for both cathode materials have been verified with experimental data. The results show that LiCoO₂ has an advantage of high maximum concentration, but the discharge drops with changes in the amount of lithium inserted. Meanwhile, LiFePO₄ has the characteristic of a flat discharge curve which gives almost the same power with changes of the inserted lithium until the cell is entirely discharged. The main factor that limits the discharge as the discharge current increases is the Lithium ion depletion region.

Development of Automatic Cloth Folding System by Using Arduino Uno Microcontroller and Servo Gears

Sahazati binti Md Rozali — 2025-02-24

The manual folding processes in various industries are prone to inefficiencies, errors, and delays due to their repetitive and labor-intensive nature. Maintaining consistency in folding techniques is also challenging, resulting in disorganized clothing and potential quality issues. Moreover, the high volumes of laundry in busy households or commercial sectors contribute to inefficient folding process. Therefore, this project proposed an automated cloth-folding system. By using this system, the problems related to folding garments can be solved quickly and neatly. The designed system utilized a combination of ultrasonic sensors, gear servo motors, a power supply, an Arduino Uno microcontroller and a flat board made of plastic with hinges or folding lines that create specific folding templates. The automated cloth-folding system successfully addresses the inefficiencies and challenges associated with manual folding processes, offering a promising avenue for improving speed and consistency in garment folding, particularly for standard-sized items.

Analysis the effect of the defect density in CdTe Solar Cell by adding Cu Layer

Mohammad Asaduzzaman Khan — 2025-02-24

Abstract – Cadmium Telluride (CdTe) solar cells hold the promise of achieving high efficiency. However, the presence of material flaws poses a significant challenge to their effectiveness. These flaws act as recombination sites, capturing electrons and holes and hindering their contribution to the current flow which reduce the performance of CdTe. Addressing these defects becomes crucial for enhancing charge carrier transport and optimizing the efficiency of these solar cells. This research paper have analyzed the effect of the defect density numerically using SCAPS-1D simulation software on SnO₂/CdTe/CdS/Cu multi-junction structural solar cell. The performance analyzing parameters of the proposed cell are optimized by varying different numerical particulars. In our analysis we have found that by varying the defect density from to the efficiency of the proposed solar cells varies from 16.5% to 17.7% with keeping same the open circuit voltage at 0.85V, short circuit current 26 mA/cm2, and fill factor 82%. The efficiency has been optimized on account of the conduction band effective density of states, =cm^-3, and valance band effective density of states, =cm^-3of the donor material (SnO₂). The cell's power conversion efficiency is optimized when the absorber has a balanced concentration of neutral, acceptor, and donor defects at 1 × 10¹⁴ cm^-3. At an operating temperature of 300 K, it is possible to achieve an efficiency of 18%.

Enhancing PV System Stability in the Angolan Power Network through Modeling and Simulation of Fuzzy Logic Controllers.

Adao Francisco Catraio — 2025-02-24

The power transmission network is responsible for transporting the electrical energy from generating plants to different substations. Electrical energy has become extremely important in this current world. Companies, churches, schools, and households need more electricity to satisfy their daily needs. This increase in demand causes several challenges to the power transmission networks in Angola such as power outages. The output power for renewable energy such as wind and solar energy systems depends on many factors such as solar irradiance, temperature, wind speed, geographical location, air density, swept area, wind turbine configurations, and many other factors. These factors are responsible for the inconsistency and variability of the wind and solar energy systems. Therefore, it is important to develop an efficient controller that can operate in different systems such as solar and wind energy systems to maintain a stable power and continuous flow of electricity. Various controllers can be used for the renewable energy system such as Unified power flow controller, Unified Interphase Power Controller, Thyristor Controlled-Interphase Power Controller, or Interphase Power Controller but in this research, the Fuzzy Logic Controllers based Maximum power point tracking (MPPT) was used to obtain a stable output power and also to mitigate the faults in the system. The proposed Fuzzy Logic Controller was introduced into a PV system which was integrated into an unstable power network to increase the PV system's performance by enabling the PV grid to generate its maximum power which is 30MW and to maintain the power network stability.

Improved Tuna Swarm Optimization (ITSO) Algorithm based on Adaptive Fitness-Weight for Global Optimization

Abdul-Fatawu Seini Yussif — 2025-02-24

In this paper, an improved variant of the Tuna Swarm Optimization (TSO) algorithm called the Improved Tuna Swarm Optimization (ITSO) algorithm is proposed. An alternative way to determine the weight (p) value to improve the convergence speed of the original Tuna Swarm Optimization (TSO) algorithm is the primary objective. This method comes along with an adaptive fitness weight strategy which is used to replace the former approach for the weight value calculation to significantly improve the algorithm’s performance on high-dimensional problems. The proposed ITSO, the original TSO, and the Harris Hawk Optimization (HHO) algorithms were implemented in MATLAB software and tested on standard benchmark test functions and the pressure vessel design optimization problem. Through extensive simulations, the ITSO algorithm exhibits exceptional performance, outperforming the TSO and HHO algorithms across a variety of test functions. When restricting the three algorithms to a maximum of 100 iterations, the ITSO algorithm achieves considerably faster convergence on approximately 84.6% of the thirteen (13) test functions. Furthermore, an engineering design problem (the pressure vessel design problem) demonstrates the superior performance of the ITSO algorithm, yielding the best cost value of 5880.9471 as compared to 5885.3327 for the original TSO and 6393.0927 for the HHO. Given the ITSO algorithm's remarkable performance relative to the TSO and the HHO algorithms, the proposed ITSO is validated as an enhanced variant of the TSO.

Real-time Implementation of a Full State Feedback Controller for a DC Motor

Kevin Love — 2025-02-24

This research work presents the real-time implementation of a Full State Feedback (FSFB) controller, which controls the angular position of a Direct Current (DC) motor. The designed Simulink controller is transformed into an object that is used in Beckhoff’s TwinCAT programming environment to interface with an actual DC motor test rig. The MATLAB command code required for the transformation is described with all other software required. The test rig used for the implementation is also shown and discussed. Lastly, a case study is done to test the control system’s response and limitations to various positional set point changes. This research contributes to the study of FSFB control for DC motors by providing a practical method to implement the simulated controller on a hardware device that is used in industry. The implemented control system can be used in industry and not just for testing purposes. This method can be used to implement any designed FSFB controlled DC motor model.