Optimal Improvement of Voltage Fluctuation Caused by High Power Photovoltaic Systems Connected to the Electrical Power Grid
Optimal Placement and Sizing of Renewable Energy Source-Based Generations in Transmission System
Simulation of Four Quadrant Fuzzy Logic Controlled Matrix Converter Fed DC Motor
Real-Time Monitoring and Control of Weaving Machines using PLC
Electricity Theft Detection in Smart Grids Based on Deep Neural Network
Design and Development Of Paddy Cutter Using Solar Energy
Design Of Double-Input DC-DC Converter (DIC) Solar PV-Battery Hybrid Power System
Comparison of Harmonics, THD and Temperature Analysis of 3-Phase Induction Motor with Normal Inverter Drive and 5-Level DCMI Drive
Application of Whale Optimization Algorithm for Distribution Feeder Reconfiguration
Detection and Classification of Single Line to Ground Boundary Faults in a 138 kV Six Phase Transmission Line using Hilbert Huang Transform
The Modeling of Analogue Systems through an Object-Oriented Design Method
Circuit Design Techniques for Electromagnetic Compliance
A Technological Forecast for Growth in Solid-State Commercial Lighting using LED Devices
Testing of Analogue Design Rules Using a Digital Interface
Simulation and Transient Analysis of PWM Inverter Fed Squirrel Cage Induction Motor Drives
The aim of this research was the optimal management of overvoltage in the photovoltaic system with the goal of maintaining voltage stability and reducing network losses. In the simulation process, a network with multiple buses and several scattered production sources was considered. These sources had varying generation capacities. Preliminary results indicated that system stability could be categorized as low, medium, or high depending on the distance between each bus and the scattered production sources. Buses located closer to the generation sources experienced minimal voltage oscillation, while those farther away showed greater fluctuations. This finding emphasized the importance of properly distributing generation to match network demand, particularly in areas where inconsistencies between production and demand led to power waste. The overvoltage levels of each distributed generation source were analyzed at individual buses, and the data was used to assess requirements across the network. The optimal distribution was aligned with network consumption and demand, allowing effective compensation for observed power deficits in certain buses through power transfer from distributed sources. To develop an optimal management model for overvoltage distribution, efforts were made to establish equilibrium among network buses and generation sources. The buses were categorized into operational modes based on their distribution profiles, and each mode was assessed for efficiency in overvoltage control. Results demonstrated that by selectively focusing on specific modes that excluded certain buses, network optimization could be improved. The findings highlighted the effectiveness of targeted overvoltage control strategies, especially in zones with higher instability.
The increased use of electricity in modern power systems is demanding the use of non-conventional energy sources to a large extent due to the limited stock of conventional energy sources. The present paper focuses on Gravitational Search Algorithm (GSA)-based optimal allocation and sizing of solar and wind power generating units in transmission systems. The performance of the optimization method has been tested on the IEEE-14 bus test system in terms of total generation cost minimization of conventional generating units, transmission line loss reduction, revenue earned from optimal generation of solar and wind power, and profit of non-conventional generating unit owners. The comparative study on the optimal installation of standalone solar, standalone wind, and a combined solar-wind system within the existing conventional transmission system has also been conducted to achieve better results.
This paper presents the concepts of a single-phase matrix converter as a universal converter for the four-quadrant operation of a DC motor. The Matrix converter is implemented as a rectifier, chopper, inverter and cyclo-converter for a high frequency step down has been presented in this paper. This will reduce the need for a new or extra converter. The technique used for the implementation of the proposed topology was sinusoidal pulse width modulation technique. This paper verifies the four possible conversion processes say AC-DC, DC-DC, DC-AC and AC-AC from a high frequency input to the desired low frequency output by the single phase matrix converter alone. The results of the four conversion topologies along with the filter has been presented in this paper. The proposed topology has been implemented in the MATLAB/SIMULINK software and the desired results for each of the converter topology has been verified.
Weaving industries rely on precise and efficient operations to ensure high-quality output and minimal downtime. This article pays attention to implementing a real-time observation and management system for textile appliances using Programmable Logic Controllers (PLCs). The system integrates sensors, actuators, and communication interfaces to automate and optimize processes like spinning, weaving, and dyeing. Parameters such as temperature, pressure, speed, and machine conditions are continuously monitored to ensure predefined standards. Real-time data acquisition and control enable swift error detection and resolution, reducing downtime and improving production. Remote monitoring allows centralized control of multiple appliances, and leverages industrial communication protocols for seamless data exchange. PLCs control essential parameters such as bearing rotation, temperature, and belt run duration in laundry machines. It calculates bearing values based on speed and belt rotation based on time, ensuring safety through DC links and automatic alarms for faults. We use supervisory Control and Data Acquisition (SCADA) for parameter monitoring. This system reduces human intervention, operational expenses, and wastage, while maintaining product quality, demonstrating PLC- based automation's potential to modernize textile industries with reliability and sustainability.
Electrical theft is a global issue that harms both utility providers and electrical users. It destabilizes utility companies' economic development, creates electric dangers, and raises energy costs for customers. The development of smart grids is significant in power theft detection because they generate huge amounts of data, including consumer usage data, which may be used to detect electricity theft using machine learning and deep learning algorithms. This study introduces a deep neural network-based classification method for detecting theft that uses a lot of data in the time and frequency domains. Data interpolation and synthetic data creation procedures are applied to address dataset shortcomings such as missing values and class imbalance. The competitiveness of the proposed strategy is demonstrated in comparison with other methods evaluated on the same dataset. During validation, the approach achieves a 90% area under the curve (ROC), which is 1% higher than the best-performing DNN currently available, and an accuracy of 94.48%, the second-highest on the benchmark.
