Voltage Stability and Power Loss Minimization with STATCOM using Particle Swarm Optimization and Hybrid Firefly Particle Swarm Optimization Algorithms
Load Frequency Control of Multi Area Power System Network with Soft Computing Algorithm Based Controller and Coordinated Strategy of TCSC and Battery Energy Storage
Performance Analysis of Distribution System under Different Loading Conditions using Test Bench
Simulation of Solar PV with Different Control System of Boost Converter for Composite Climate
Enhancement of System Performance using PeSche Scheduling Algorithm on Multiprocessors
Multi Area Load Frequency Control of a Hybrid Power System with Advanced Machine Learning Controller: Case Study of Andhra Pradesh
A New Hybrid Cuckoo Search-Artificial Bee Colony Approach for Optimal Placing of UPFC Considering Contingencies
Efficiency and Investment Comparison of Monocrystalline, Polycrystalline, and Thin Film Solar Panel Types at Karabuk Conditions
Design of a Grid Connected PV System and Effect of Various Parameters on Energy Generation
Comparative Analysis of Harmonics by Shunt Active Filter using Resonant Current Control in Distribution System
Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI
Development of Power Flow Controller for Grid Connected Renewable Energy Sources Using Lyapunov function
Detection and Location of Faults in Three Phase 11kv Underground Power Cables By Discrete Wavelet Transform (DWT)
Design of PV-Wind Hybrid Micro-Grid System for Domestic Loading
Applications of Artificial Neural Networks in various areas of Power System; A Review
In this research a new approach is introduced for detecting and classifying faults in a Thyristor-Controlled Series Capacitor (TCSC) compensated power system. Our proposed scheme utilizes both the Fast Walsh Hadamard Transform (FWHT) and machine learning algorithms. The FWHT is employed to extract fault features from current data obtained from the TCSC compensated transmission line, while machine learning algorithms such as K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are used to classify the extracted features for the purpose of fault detection and identification. To evaluate the performance of our proposed scheme, simulation studies were conducted on a test system under various fault conditions. The simulation results demonstrate that our approach is highly effective in accurately and quickly detecting and classifying faults, even when noise and TCSC compensation are present. This scheme has the potential to enhance the reliability and efficiency of power transmission systems.
Electric Vehicles (EVs) have gained popularity in recent years due to its sustainability. However, the charging process for EVs can be inconvenient and time-consuming, particularly when relying on conventional charging stations that necessitate manual vehicle connections. This research aims to develop a wireless charging system for electric vehicles that incorporates position alignment technology. The objective is to enhance the charging experience for EV owners, improving efficiency while reducing the environmental impact associated with traditional charging methods. The system utilizes a combination of wireless charging technology and sensors to detect the position of the EV and align it with the charging pad using Internet of Things (IoT) technology. This ensures an optimized charging process, enabling quick and efficient charging of the EV.
In recent years, solar photovoltaic (PV) units have rapidly grown to become a major contributor to the installed generating capacity worldwide. However, the intermittent and fluctuating nature of renewable generation can negatively impact power quality and generating units. The backbone of these problems is often related to the control system of the entire network. In a solar-based generation, extracting maximum power known as Maximum Power Point Tracking (MPPT), is a major challenge due to its dependence on climatic conditions and location. This research aims to review the steps taken to enhance output by using different control systems for maximum power point extraction and to achieve optimization in the control system technique, resulting in the best output of any location's current solar radiation scenario.
This research aims to improve the stability of power systems using a power stabilizer. Various methods were used to finetune the conventional Power System Stabilizer (PSS) with a lead-lag compensator by minimizing the integral absolute error of speed deviations of generator rotors. To evaluate the performance of the various methods, different timedomain simulation test cases were conducted and the results were compared with the performance of the Oppositional Whale Optimization Algorithm-based Power System Stabilizer (OWOA-based PSS), Whale Optimization Algorithm-based Power System Stabilizer (WOA-based PSS), and the conventional PSS. The obtained results show that the OWOA-based PSS is more efficient in power oscillation damping than the other methods, including the WOA-based PSS. Overall, the OWOA-based PSS can be considered as a potential solution to enhance the stability of power systems by mitigating power oscillations in generator rotors. However, further studies and experiments may be required to validate all methods and compare them to ensure their effectiveness and efficiency.
The increasing electrification of daily life and the growing number of sensitive or critical loads have led to a rising demand for high-quality electricity and overall energy consumption. Customer demands for increased generating capacity and efficient energy production, distribution, and utilization are driven by the global increase in energy consumption and the depletion of fossil resources. The concept of the microgrid holds promise for addressing significant issues arising from the widespread use of distributed generation in distribution networks. To ensure successful microgrid operation, an appropriate control strategy is essential. MATLAB and other software can be used for simulating and evaluating various load models. This article provides an overview of the previous research on microgrids.
