Optimal placement and sizing of renewable energy source-based generations in transmission system
Optimal improvement of voltage fluctuation caused by high power photovoltaic systems connected to the electrical power grid
Simulation of Four Quadrant Fuzzy Logic Controlled Matrix Converter Fed DC Motor
Real-Time Monitoring and Control of Textile Industries 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 increased use of electricity in modern power system is demanding the use of non-conventional energy sources in 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 system. The performance of optimization method has been tested on 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, profit of non-conventional generating unit owners. The comparative studies of optimal installation of only solar, only wind and both solar and wind in existing conventional transmission system have been also studied for better results.
The aim of this research was the optimal management of overvoltage in the photovoltaic system with the aim of maintaining voltage stability and reducing network losses. In the simulation process, we considered the number of buses and 10 scattered production sources as the path and simulation process. We considered the number of 10 scattered production sources with a capacity between 18 and 25 MW. Examining the preliminary results of the system has shown that the range is low Medium and high stability is considered based on the distance between each bus with scattered production sources. So that basses 1 to 3 have the lowest range of oscillation because they are located in the closest distance to the production sources. Similarly, basses 7 to 17 have more distances than the production source and have more fluctuation. In order to meet the needs of the network for optimumThe distribution of the production of scattered production sources, which usually have non-constant conditions, especially from bus 7 onwards, it is possible to observe the amount of waste in the network due to the lack of coordination of the scattered production with the network demand, we used the optimal management of overvoltage . The amount of overvoltage of each distributed generation source varies from 25 MW to 18 MW and in each bus this requirement is investigated in the network. and the measurement has been placed. This optimal distribution rate was matched with the amount of consumption and demand of the network in overvoltage and we showed that, for example, in the first bus, the amount of demand of the network is 800 megawatts and the amount of production of the main network in overvoltage is equal to 770 megawatts and a deficit of 30 megawatts has been observed. Using the optimal management of overvoltage of 25 megawatts of the entire networkFrom scattered productions, the power is transferred with this bus to compensate the deficit to a large extent. In the following, in order to formulate an optimal management model for overvoltage distribution, the establishment of a balance point for the activity of network buses along with the ten sources of distributed generation has been investigated. For this purpose, by categorizing all network buses into four modes that include all buses, each mode (average We compared multiple bus sets) in each bus and showed that in the first bus and the first mode, the optimization rate of overvoltage control management was equal to 68.73% and in one turn, not considering the first mode for the bus network and only in Considering the fourth mode, which includes buses 7 to 17, the amount of network optimization has increased by 11.71%. In other words, in the fourth mode Without having six buses, we were able to optimize the network by 11.71% with the help of overvoltage control management.
This paper will present the concepts of single phase matrix converter as an universal converter for four quadrant operation of DC Motor.Matrix converter is implemented as an rectifier,chopper,inverter and cyclo-converter for a high frequency step down has been presented in this paper. This will reduce the need for the new or an extra converter requirement. 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.
Textile industries rely on precise and efficient operations to ensure high-quality output and minimal downtime. This article paying attention on implementing a real-time observation and manage 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 acquirement and control enable swift error detection and resolution, reducing downtime and improving production. Remote monitoring allows centralized control of multiple appliances, leveraging industrial communication protocols for seamless data exchange .PLC controls 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. Supervisory Control and Data Acquisition (SCADA) is used for parameter monitoring. This system reduces human intervention, operational costs, 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 research presents a theft detection system that employs extensive information in the time and frequency domains in a deep neural network-based classification approach. We use data interpolation and synthetic data creation procedures to overcome dataset shortcomings such as missing data and class imbalance issues. Finally, we demonstrate the competitiveness of our strategy when compared to other methods assessed on the same dataset. During validation, we achieved 90% area under the curve (ROC), which is 1% greater than the best DNN in current works, and 94.48% accuracy, which is the second-highest on the benchmark.
