Thermodynamic and Exergoeconomic Operation Optimization and Simulation of Steam Generation Solar Power Plant
Topology Transformation Approach for Optimal PMU Placement for Monitoring and Control of Power System
Performance Evaluation of Power System with HVDC Integration: Impact of SSSC and STATCOM on Power System Efficiency and Stability
Photovoltaic Systems: A Pollination-Based Optimization Approach for Critical Industrial Applications
Design of a Robust Controller for the Load Frequency Control of Interconnected Power System
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
This study proposes a Load Frequency Control (LFC) approach for Multi-Microgrids (MMGs) using the World Cup Optimization (WCO) algorithm. The approach aims to improve the frequency regulation of MMGs while maintaining the power balance between generation and load. The LFC system consists of a Proportional-Integral (PI) controller and a Proportional Integral Derivative (PID) controller, which are tuned using the WCO algorithm to optimize the controller parameters. The effectiveness of the proposed approach was evaluated using a test system comprising of two interconnected MMGs. The simulation results demonstrate that the proposed LFC approach using the WCO outperforms other optimization algorithms and effectively maintains the frequency within acceptable limits during various load variations and disturbances. Additionally, the proposed approach ensures power balance among the MMGs, leading to a stable operation of the entire power system.
Power generation and renewable energy consumption benefit greatly from integrated solar grid systems. However, the intermittent nature of solar electricity and potential voltage swings might raise power quality concerns when solar energy is integrated with the grid. The benefits of the split-source inverter architecture include higher efficiency, lower harmonic distortion, and the ability of power flow in both directions. The ANFIS controller improves the control parameters of the single-stage split-source inverter, eliminating power quality issues including voltage fluctuations, harmonics, and reactive power variations. These difficulties can negatively affect the overall quality of power. ANFIS-based control of the solar-grid integrated system guarantees optimal power conversion, precise voltage management, and higher power quality by intelligently altering the control settings. ANFIS solar electricity and potential voltage swings might raise power quality concerns when solar energy is integrated with the grid. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS)- based control with split source inverters, this research provides a unique method to improve power quality in solar-grid integrated systems. The benefits of the split-source inverter architecture include higher efficiency, lower harmonic distortion, and the ability for power to flow in both directions. The ANFIS controller improves the control parameters of the single-stage split-source inverter, eliminating power quality issues including voltage fluctuations, harmonics, and reactive power variations. These difficulties can negatively impact the power's overall quality. ANFIS-based control of the solar-grid integrated system dynamically adjusts the voltage and current waveforms in response to variations in solar irradiation and grid circumstances. The simulation results show that the harmonic distortion is lowered, voltage stability is enhanced, and reactive power is managed more effectively.
Phasor Measurement Units (PMUs) have emerged as crucial tools in the field of power system monitoring, control, and safety. Extensive research has been conducted on enhancing the capabilities of PMUs. Traditional SCADA techniques, which have been employed for many years, gather data on power system parameters at intervals of 4 to 10 seconds, offering a static perspective of the system's behavior. However, relying solely on static data may limit the effectiveness of the monitoring and control. On the other hand, PMUs provide real-time information about the dynamic state of the system, enabling more comprehensive insights. For the optimal and cost-effective installation of PMUs, it is vital to strategically position them in locations that effectively monitor the state of the system. The Instruction Level Parallelism (ILP) approach was employed to identify the ideal PMU placement for power system observability. The ILP approach determines the most suitable positions for PMUs by considering factors such as the redundancy of measurements and cost efficiency. To validate the effectiveness of the ILP approach, it was tested using the widely recognized IEEE 14 bus system, which serves as a standard benchmark for power-system analysis. Through the ILP approach, the optimal placement of the PMUs can be determined, thereby enhancing the observability of the power system. This in turn improves situational awareness, enables effective fault detection, and facilitates coordinated control strategies. By integrating PMUs and the ILP approach, the power system monitoring and control can be significantly enhanced. Ongoing research and development in PMU technology and placement methodologies continues to advance the field, enabling more efficient and effective management of modern power grids.
Renewable energy sources have emerged as a potential option to fulfill the growing need for power because they are less harmful to the environment and are abundantly available in nature. Among various alternative energy sources, Photo Voltaic (PV) systems have gained increasing popularity because of the abundant supply of solar power. The maximum power point tracking technique was employed to maximize the power output of the PV array. However, when transferring a significant amount of power from a PV array to the power grid, several power quality concerns must be addressed, particularly those related to the actual and reactive power flows. Connecting photovoltaic arrays to the power grid inevitably leads to power quality issues. This study proposes a novel control approach aimed at tackling these problems and effectively managing the flow of electricity. To achieve efficient regulation of the actual and reactive power flows in grid-connected solar systems, a fuzzy Genetic Algorithm (GA) cascaded controller is utilized in conjunction with a flexible AC transmission system device known as the Unified Power Flow Controller. The fuzzy logic controller generates a control vector as its output, which is fine-tuned using a GA technique.
This study presents a comparative analysis of fault detection and categorization methods in TCSC (Thyristor Controlled Series Capacitor)-compensated power systems. The analysis explores different techniques for extracting fault features from current data obtained from TCSC-compensated transmission lines. Machine learning algorithms, namely kNearest Neighbor (k-NN) and Support Vector Machine (SVM) are employed to categorize the extracted features, enabling fault detection and identification. To evaluate the efficacy of the proposed method, simulation tests were conducted on a test system, considering various fault scenarios. The simulations accounted for the presence of noise and TCSC correction, reflecting real-world operating conditions. The results demonstrate the efficiency of multiple approaches in accurately and swiftly detecting and classifying faults. By leveraging the capabilities of machine learning algorithms and extracting relevant features from current data, the proposed method offers a promising solution for fault detection and categorization in TCSC-compensated power systems. The ability to operate effectively even in the presence of noise and TCSC correction highlights the robustness and reliability of the proposed approach.
