Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 29, 2024
Language: Английский
Electric Power Systems Research, Journal Year: 2025, Volume and Issue: 243, P. 111488 - 111488
Published: Feb. 8, 2025
Language: Английский
Citations
3
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 2, 2025
Language: Английский
Citations
2
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 13, 2025
In this paper, explore the effectiveness of a new Wide Area Fuzzy Power System Stabilizer (WAFPSS), optimized using Exponential Distribution Optimization (EDO) algorithm, and applied to an IEEE three-area, six-machine power system model. This research primarily focuses on assessing stabilizer's capability dampen inter-area oscillations, critical challenge in grid operations. Through extensive simulations, study demonstrates how WAFPSS enhances stability reliability under variety operational conditions characterized by different communication delay patterns. The application proposed stabilizer specific model provides detailed insight into its performance real-world scenarios, illustrating adaptability managing dynamic disturbances. simulation results reveal that achieves significant reductions Integral Time Squared Error (ITSE), with improvements 94.1%, 97.02%, 98.18% three distinct cases, showcasing superior damping robustness. findings indicate advanced optimization techniques provided EDO algorithm significantly improve response, ensuring robust performance. integration WAMS sophisticated control systems fuzzy logic presents strategic solution complexities faced modern networks, optimizing their face increasing renewable fluctuating demand.
Language: Английский
Citations
2
The Journal of Engineering, Journal Year: 2025, Volume and Issue: 2025(1)
Published: Jan. 1, 2025
Abstract This paper introduces a comprehensive cascaded controller to enhance transient and dynamic stability in load frequency control for low‐inertia multi‐area power systems (LIMAPSs) integrating solar photovoltaic wind turbine sources. The proposed method combines composite energy storage‐based virtual synchronous generator with novel employing an adaptive neuro‐fuzzy inference system (ANFIS) fractional‐order proportional–integral proportional‐tilt‐integral (FOPI‐FOPTI) strategy. Firstly, model of thermal plant incorporating photovoltaic, turbine, is developed. Subsequently, the ANFIS‐FOPI‐FOPTI parameters, optimized via whale optimization algorithm, provide robust under disturbances. Finally, extensive simulations demonstrate approach's effectiveness enhancing LIMAPSs. are conducted on two‐area modified IEEE 10‐generator 39‐bus system, considering disturbances like stochastic load‐generation. Comparative analysis existing controllers shows effectively addresses challenges It outperforms other approaches terms settling time, overshoot, specified objective functions, lowest integral time absolute error values recorded: 0.1202 0.480 four‐area system.
Language: Английский
Citations
1
Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 24, 2025
Language: Английский
Citations
1
Results in Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 102159 - 102159
Published: Feb. 1, 2025
Language: Английский
Citations
0
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: Dec. 28, 2024
Autonomous microgrids (ATMG), with green power sources, like solar and wind, require an efficient control scheme to secure frequency stability. The weather locationally dependent behavior of the sources impact system imperfectly. This paper develops intelligent, i.e., fuzzy logic-based sliding mode (F-SMC) utilizing a proportional-integral-derivative (PID) type surface regulate wind-diesel generator-based ATMG system. A dynamic structure wind generator is designed participate in support considered plant. mastery F-SMC analyzed over conventional SMC (C-SMC) under load perturbation. study used artificial gorilla troop optimization (GTO) technique tune parameters. effectiveness GTO-tuned regulation (FR) compared well-established particle swarm (PSO) grey wolf (GWO) approaches various scenarios such as perturbations, governor dead band (GDB), generation rate constraint (GRC), higher/lower dimensions ATMG, speed variations. Finally, proposed GTO-based approach has been validated upon standard IEEE-14 bus recent techniques.
Language: Английский
Citations
3
Engineering Reports, Journal Year: 2025, Volume and Issue: 7(1)
Published: Jan. 1, 2025
ABSTRACT Power system stability is crucial for the reliable and efficient operation of electrical grids. One key factors affecting power frequency alternating current (AC) while connected with High Voltage Direct Current (HVDC) transmission system. Changes in load demand can lead to deviations, which have detrimental effects on performance Frequency should therefore be controlled within predefined limits order prevent unexpected disturbances that may cause problems loads or even entire fail. A broad simulation model HVDC developed using MATLAB software evaluate effectiveness proposed controllers such as Adaptive Neuro‐Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), optimization Proportional‐Integral‐Derivative (PID) controller Particle Swarm Optimization (PSO) based control strategy addressing instability problems. To assess how well ANFIS, ANN, PID‐PSO controls system, several situations were simulated, including changes operational circumstances. The result reveals ANN performs more accurate results than other and, displaying its capacity successfully reduce deviations maintained a 50 Hz. Adopted method suggested easy integration AC grid enhances quality stability.
Language: Английский
Citations
0
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Feb. 8, 2025
Hybrid energy systems (HESs) are integrated that have successfully addressed the problems of meeting increasing demand for electrical power. Like all known power systems, and stream quality among most important issues in addition to durability HES. In this study, battery-powered HES is presented, where designed system consists a wind photovoltaic (PV) system. The strategy maximum point (MPP) tracking (MPPT) based on adaptive neuro-fuzzy inference (ANFIS) method used command PV system, MPPT neural used. These proposed strategies do not need mathematical model studied augment robustness stability, performance great. Also, fractional-order proportional-integral regulator integral sliding mode control approach combined battery-based storage particle swarm optimization was estimate gain values resulting controller. realized using MATLAB, competence tested under different work scenarios. results showed excellent efficacy were compared with conventional control. simulation case speed being 12 m/s, rise time, response MPP, steady-state error (SSE) improved by rates estimated at 99.32%, 60%, 1.5%, respectively perturbations observations-based approach. Compared traditional strategy, ANFIS-MPPT improves SSE, time irradiation, which takes value 1000 W/m2, percentages 18%, 94.70%, 69.23%, respectively. PSO-FOPI-ISMC harmonic distortion current second test 55.20% 72.90% 1 2, respectively, make great importance future other industrial applications.
Language: Английский
Citations
0
Sustainable Energy Research, Journal Year: 2024, Volume and Issue: 11(1)
Published: Nov. 22, 2024
Abstract In modern energy systems, managing within a microgrid (MG) poses significant challenges due to the unpredictable nature of renewable sources. This article introduces novel approach for optimal battery management in photovoltaic–wind using Modified Slime Mould Algorithm (MSMA) combined with fuzzy-PID controller. The comprises wind turbine (WT) generator, solar photovoltaic (PV) and storage system (BESS). BESS plays crucial role meeting high power demand during outages, while controller ensures accurate prediction battery’s state charge (SOC). proposed method’s performance is evaluated by comparing MSMA-based PSO-based establish its effectiveness. achieved fine-tuning MSMA algorithm. Simulation results demonstrate that (BMS) effectively optimizes charging discharging based on availability load demand. adjusts operation minimizing error between desired actual voltage. Performance validation has been conducted RTS-lab five distinct scenarios—45 kW, 35 75 4.5 12.5 which confirming effectiveness control strategy management.
Language: Английский
Citations
2