PV-grid Performance improvement through Integrated Intelligent Water Drop Optimization with Neural Network for Maximum Power Point Tracking
A Digital Healthcare Monitoring System with Real-Time Analysis
Advancements in Smart Meter Design and Integration for Enhanced Energy Management and Efficiency
Electric Vehicles in Modern Transportation: Environmental Impacts, Configurations, and Future Trends – A Review
GTO Technique Based Hybrid Power System Controller Design
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
In this paper, an improved variable Maximum Power Point Tracking (MPPT) method for photovoltaic (PV) water pumping system is proposed. This method consists on a modified single-voltage-sensor based MPPT algorithm, which is used to fine tune the duty cycle of a DC/DC converter in order to avoid divergences of the MPP under varying irradiance levels. Furthermore, using this technique, only the output voltage of a power converter needs to be sensed in order to find and track the MPP. To investigate the performance of the proposed MPPT method, a PV system that includes solar array, DC/DC buck converter, DC motor-pump, and MPPT control system is considered and simulated on MATLAB platform. The proposed variable step-size method is compared to perturbation and observation classical method with fixed step-size. The simulation results are given and discussed for validation.
The demand for electricity has been growing day by day. To satisfy the demand, new power stations are being established and are interconnected to the grid, due to which the transmission lines become overloaded. Therefore, it is very essential to control the power flow in the upcoming grids. To fulfil the requirements, FACTS devices are employed. Among all the devices, Unified PFC is one of the main adaptable FACTS (Flexible AC Transmission System) controllers, because it offers finest performance characteristics. However, due to its configuration UPFC is treated as the most expensive controller. A new and cost effectual voltage source converter based FACTS converter has been proposed in this paper, which uses mostly, the existing inactive components and are consequently considered as hybrid in nature. Due to the usage of inactive components, HPFC is termed as symmetrical Hybrid PFC. The three dissimilar HPFC (Hybrid Power Flow Controller) configurations are employed on a two-machine system and they are designed in Matlab/Simulink. By comparing the results, it can be proved that HPFC is one of the most superior options for eliminating the power oscillations and thereby improving the power system stability.
The difficulties in obtaining the Right-of-Way have led to the development of multi-terminal transmission systems and these transmission systems are more vulnerable to faults and required to be cleared immediately as soon as they do occur for minimizing the power system disturbances and for efficient transmission. This paper aims in the systematic approach for fault detection, classification, faulty terminal identification, and fault location estimation of a four-terminal transmission system compensated with Unified Power Flow Controller (UPFC) based on frequency domain approach employing wavelet multi resolution analysis with the variations in fault inception angle, fault distance, and fault impedance by extracting the fault indices of current signals of all the three phases at all terminals. These fault indices were then compared with threshold value to detect and classify the faults and also identify the faulty terminal. The fault indices were used as inputs to the Fuzzy Inference System (FIS) for approximate fault location estimation from the respective terminals. Bior 1.5 wavelet has been chosen as mother wavelet as it has given the accurate results when compared with other members of wavelet family. The entire simulation was carried out in MATLAB environment within 110 km, 400 KV four-terminal transmission system, and the classification and location algorithms can be used as effective tools for real time digital relaying purpose as it has taken less than half cycle to detect the faults.
In an electrical network, an optimal procedure is required for resolving the problems like voltage profile improvement and power loss reduction. Now-a-days, the condition for optimality can be attained by effective and efficient utilization of available facilities with add-on FACTS devices in the power systems. In practical applications, the choice of Static VAR Compensator (SVC) is better than other devices, as for the analysis and mitigation of problems due to its applicability and affordable costs. In this paper, for the selection of optimum location of SVC, determination of SVC's size and SVC's firing angle - three heuristic Optimization algorithms are proposed as evolutionary optimizing algorithms for the same objective function, namely, Genetic Algorithm, Particle Swarm Optimization, and Dragonfly algorithm. To show the validity of the proposed techniques and for comparison purposes, simulations are carried out on IEEE 14 and IEEE-57 bus power system assessing improvements in voltage profile and reducing power losses in order to present its adaptability in power systems of higher dimensional.
Genetic Algorithms (GAs) are a module of evolutionary computing, which is a rapidly developing domain of artificial intelligence. These algorithms are inventive by Darwin's theory about Darwinism. Naturally said, solution to a problem solved by GAs is evolved. In order to find an effective way to use GA widely, the basic knowledge of GA was introduced. After the introduction of its development, characteristic and application, the trends of its modification and application were analyzed. This algorithm is a optimization and search method for simulating natural choosing and genetics. This paper gives a brief introduction to genetic algorithms, its operators, and encoding techniques. This study has significance in theory of GA.