International Journal of Electrical Engineering and Applied Sciences (IJEEAS)

Table of Contents Vol. 6 No. 2 (2023)

2024-03-05T02:44:09+00:00

Table of Contents Vol. 6 No. 2 (2023)

Design and Implementation of Remote-Controlled Lawn Mower Using Dual-Tone Multi-Frequency Technology

2023-03-01T00:05:26+00:00

One of the most important machines that assist with retaining the physical shapes and beauty of lawns is lawn mower. There are ranges of styles and shapes for commercial use of heavy-duty mowers to residential use of push mowers. Lawn mower can operate on solar power, electricity or diesel. Some lawn mowers have the capabilities to gather the cut grasses into bags and spread the grasses on the surfaces of the soil to help reduce loss of moisture in the soil. Lawn mowing requires the operator to move to the various locations that the weeding would take place. This causes fatigue to the operator since the operator would have to be standing and moving in the sun for a long time and also could be hit by a stone during the cutting of the weeds. This could lead to injuries to the operator and staying in the sun for long can likewise cause migraine. Furthermore, the exposure to noise from the mower and dust may also be harmful to the health of the operator. To overcome these challenges associated with lawn mowers, their operation can be automated. This paper aims to design and construct a prototype of a remote-controlled lawn mower using dual-tone multi-frequency technology to control the directions of a lawn mower. This research was designed using Arduino Uno, which is based on the ATmega328P microcontroller, 6 VDC geared stepper motor, a 700- HB relay that serves as a motor driver, a sim8001 DTMF module integrated circuit (IC) and a mobile phone

Stability Evaluation of Non-ideal Grid-tied Photovoltaic on IEEE-9 Bus System

2023-06-02T03:12:12+00:00

The integration of photovoltaic in power systems has proved its capability of being a viable source of power to be considered in grid-tied power systems. However, in some cases various electrical challenges may arise when high photovoltaic power is integrated into power systems at the time the voltage collapse caused by an increase in the load consumption. In case of non-ideal grid-tied photovoltaic due to inappropriate or absence of control strategies to regulate the power dispatch between the microgrid and the grid, the photovoltaic power may negatively further affect the grid voltage stability instead of restoring it to the stability level of the electrical grid. This paper investigates the challenge of solar power integration in the grid by demonstrating that due to its randomness and intermittent nature, the photovoltaic power is likely to be associated with voltage disturbance and low power quality that occurs on the electrical grid. Data from simulation of a non-ideal grid-tied photovoltaic are collected to demonstrate the characteristics and negative effects of high photovoltaic integration on the IEEE-9 bus system.

Optimal Integration of Multiple Shunt Reactive Compensators in Radial Distribution Systems for Loss Reduction using Modified Mountain Gazelle Optimizer (MMGO)

2023-07-05T00:57:45+00:00

This paper proposed a two-step approach for reducing power losses using a Modified Mountain Gazelle Optimizer (MMGO). First, the loss sensitivity factor is used to identify the most promising locations for shunt reactive compensators. This approach reduces the search space and improves the efficiency of the optimization process. Secondly, the proposed MMGO algorithm is applied to determine the optimal locations and sizes of the shunt compensators. The performance of the proposed method is evaluated on standard IEEE 33-bus and IEEE 69-bus systems and compared with other optimization techniques reported in the literature. The proposed MMGO produced 34.05% and 35.45% reductions in active power losses in the IEEE 33 and 69 bus systems respectively. Simulation results demonstrated the effectiveness of the two-step approach in achieving significant loss reduction and improving voltage profiles. The findings of this research provide valuable insights for distribution system operators and pave the way for more efficient and reliable power distribution networks.

Comparison of RRT and TRRT Object Arrangement Path Planning Robot for Retail Warehouse Using CoppeliaSim

2023-07-07T01:51:18+00:00

Shelf stocking in retail warehouses requires high accuracy, multitasking, and precise timing. Automating the process has become a popular solution due to difficulty in facing continuous demand in replenishing items at shelves. The traditional path planning, Rapid-exploring Random Tree (RRT) approach is inefficient and produce suboptimal path generation when it comes to complex workspaces such as retail warehouse. Furthermore, the simple linear interpolation method used for robotic arm manipulation can result in jerky, unrealistic motion, and collisions with obstacles. To address these issues, the Transition-based Rapidly-exploring Random Tree Star (TRRT) is proposed to achieve the shortest distance in shelving items at warehouse. The study also implements inverse kinematics algorithms for smooth arm manipulation. In this study, the TRRT is constructed in CoppeliaSim software using the OMPL Library as well as Inverse Kinematics (IK) algorithms. The results show that the TRRT approach generates shorter paths than the RRT approach but takes longer calculation time. Moreover, the TRRT approach demonstrates a good repetition result compared to the RRT approach. Furthermore, the accuracy percentage between the joints angle obtained from CoppeliaSim and the IK calculation in MATLAB reaches 99.27%. In conclusion, the TRRT approach and inverse kinematics algorithms improve the efficiency and smoothness of the object arrangement path planning robot, making it a viable solution for automating the shelf-stocking process in retail warehouses.

Green Auto Mobile Vehicle Design

2023-10-17T11:15:14+00:00

The grid line power crisis is very common in a developing country like Bangladesh. Due to the development of roads and public demands, the number of electric transports are increasing. This has caused for the increase in power consumption for charging the battery of that vehicles. Therefore, to reduce the power consumption of the national gridline for charging electric vehicles, solar energy-based vehicle such as solar energy-based auto-rickshaw, auto-cars are desired. Furthermore, the global warming is increasing due to pollution, deforestation, over uses of fuel-based vehicles, running of industries and etc. Depletion of the ozone layer due to greenhouse gas releases from vehicles will be one of the world's main problems in the future. So, it would be a solution to the above-mentioned issues if solar cars were introduced. This research project aims to develop, implement and deal with the aspects of the photovoltaic vehicle that play an essential role in ensuring a future without energy crisis.

Enhancing Photovoltaic System Efficiency Through Fuzzy Logic-Based Maximum Power Point Tracking

2023-10-16T00:18:35+00:00

This study utilizes a fuzzy logic controller (FLC), a type of computational intelligence tool, for determining a photovoltaic (PV) system's maximum power point (MPP). The solar PV system comprises of a PV module, an MPP controller, all of which are designed to monitor and optimize the highest point of energy generation in a photovoltaic system and a DC-DC boost converter. FLC is the core methodology employed in this study for maximum power point tracking (MPPT) within the converter-supplied PV system. The MPPT process involves the analysis of system parameters, including current error and the rate of change of errors, which serve as the input to the fuzzy logic system. The FLC continuously processes these inputs to adjust the duty cycle of the DC-DC boost converter, ensuring that the PV system operates at its highest energy generation potential. The 1Soltech 1STP-215-P PV model is utilized for this study, and it is representative of the PV model used in simulation studies. Photovoltaic systems are inherently nonlinear and respond to various external factors, making them less efficient. Therefore, the proposed intelligent control method, based on artificial intelligence principles, offers an easily implementable solution and provides valuable feedback. It exhibits robust performance even when solar irradiance levels fluctuate and excels at tracking MPPs, among other advantages. To simulate and evaluate the maximum power point tracking in a solar system, a PV system incorporating a fuzzy logic controller is designed and simulated using MATLAB/Simulink. The simulation results confirm the effectiveness of the proposed fuzzy logic controller in maximizing electricity generation from the PV panels while maintaining their operating voltage at an efficient level.

Developing Resilient and Expandable Adaptive Capacity Arbitration Algorithms for Future WCDMA (UMTS) Wireless Systems

2023-10-02T07:01:26+00:00

The objective of this research paper is to tackle the emerging challenges associated with resource management in future WCDMA (UMTS) wireless systems by presenting resilient and expandable adaptive capacity arbitration algorithms. The escalating demands for wireless communication necessitate effective resource allocation, ensuring optimal performance and user satisfaction. Accordingly, this paper introduces innovative algorithms designed to dynamically distribute resources based on user requirements, channel circumstances, and Quality of Service (QoS) preferences. By conducting an extensive analysis of pertinent literature, this work identifies the limitations inherent in current resource allocation strategies applied in WCDMA/UMTS systems. The proposed algorithms place a strong emphasis on achieving resilience by taking into account interference issues, uncertainties, as well as evolving network conditions. Furthermore, the algorithms have been designed to address concerns related to scalability so that they can efficiently handle a growing number of users and devices. The approach involves developing these algorithms and then evaluating their performance comprehensively using simulation tools. The results indicate that the adaptive capacity arbitration algorithms proposed outperform existing methods in terms of throughput, latency, and resource utilization. These findings suggest that the algorithms have great potential to greatly enhance the efficiency and reliability of future wireless systems. In short, this research paper makes a valuable contribution to the field of wireless communication by presenting innovative adaptive capacity arbitration algorithms specifically tailored for WCDMA/UMTS wireless systems. With the demonstrated robustness and scalability, these algorithms hold significant promise in revolutionizing resource management within wireless networks, thereby paving way for better connectivity and enhanced user experiences. Avenues that could be explored in future research involve the practical application of these algorithms in real-world contexts and improving their efficiency under different network conditions.

Enhanced Adaptive Simulated Based Artificial Gorilla Troop Optimizer for Global Optimisation

2023-09-22T07:44:24+00:00

The enhancement of metaheuristic algorithms has been considered an important step in improving the solution quality of systems. In this paper, a modification to the traditional Gorilla Troop optimizer is proposed. The modification leverages the powerful properties of circle chaotic mapping and step adaptive simulation. The conventional algorithm, while effective in certain scenarios, exhibits limitations in handling complex and dynamically changing data sets. To address these shortcomings, a three-fold approach is proposed to enhance its performance. A circle chaotic mapping is integrated into the algorithm's initialization phase to enhance its sensitivity to initial conditions. The chaotic mapping effectively diversifies the search space, facilitating improved exploration and convergence to optimal solutions. Secondly, a step adaptive simulation is introduced as a means to dynamically adjust the simulation steps during runtime. Finally, the concept of adaptive simulation based on the state of the silverback gorilla (best solution) in the troop is used to simulate the exploitation phase to help the ASGTO overcome local optima entrapment and produce better solutions. The performance of the proposed ASGTO was assessed on twenty-two benchmark optimization functions and compared with the standard GTO, grey wolf optimizer (GWO), and whale optimization algorithm (WOA). The results showed that the proposed ASGTO outperformed the standard GTO, GWO, and WOA. ASGTO, GTO, GWO, and WOA attained global optimum values for 82%, 77%, 55%, and 59% of the 22 benchmark functions respectively. Consequently, the modified algorithm exhibits robustness and adaptability, making it applicable across various domains. The ASGTO is therefore recommended for adoption in solving optimization problems.