Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: May 10, 2025
Language: Английский
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 8, 2025
This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with Genetic Algorithm-optimized PID (GA-PID) controller. Traditional controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent interconnected leading to slower settling time higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates these issues by utilizing LSTM's capacity learn from historical data gradient descent forecast future disturbances, GA optimizes parameters real time, ensuring dynamic adaptability improved control precision. proposed controller's performance is rigorously tested against both classical controllers through simulations conducted MATLAB/Simulink. results reveal that achieves 2.33-fold reduction compared 4.07-fold Additionally, exhibits 3.27% mechanical output perturbations 3.43% transient changes. Hardware validation has been carried out show robustness of model.
Language: Английский
Citations
1
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Feb. 17, 2025
The rapid growth in power demand, integration of renewable energy sources (RES), and intermittent uncertainties have significantly challenged the stability reliability interconnected systems. electric vehicles (EVs), with their bidirectional flow, further exacerbates frequency fluctuation system. So, to mitigate & deviations as well stabilize system integrated distributed generators (DGs) EVs, robust intelligent control strategies are indispensable. This study dedicates a novel Fuzzy-Sliding Mode Controller (FSMC) utilized for load (LFC). First, dynamic response has been evaluated by using Sliding (SMC), showcasing its robustness against external disturbances parameter uncertainties. Second, enhance performance, fuzzy logic is SMC, leveraging adaptability create FSMC controller. achieved superiority handling non-linearities, communication delays variations A significant contribution like design tuning controllers Modified Gannet Optimization Algorithm (MGOA) established. potential MGOA over GOA corroborated convergence speed precision through benchmark functions. Furthermore, paper extensively analyzes impact EV tie-line dynamics under varying regulation capacities uncertain operating conditions. Comparative studies demonstrate that MGOA-tuned achieves faster settling times, reduced overshoot, improved metrics compared conventional state-of-the-art methods. Finally, MATLAB-based simulation results validated real-time implementation on OPAL-RT 4510 platform, confirming practicality proposed methodology addressing modern challenges involving high penetration integration.
Language: Английский
Citations
1
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Feb. 21, 2025
Abstract This article presents the Flower Fertilization Optimization Algorithm (FFO), a novel bio-inspired optimization technique inspired by natural fertilization process of flowering plants. The FFO emulates behavior pollen grains navigating through search space to fertilize ovules, effectively balancing exploration and exploitation mechanisms. developed is theoretically introduced rigorously evaluated on diverse set 32 benchmark problems, encompassing unimodal, multimodal, fixed-dimension functions. algorithm consistently outperformed 14 state-of-the-art metaheuristic algorithms, demonstrating superior accuracy, convergence speed, robustness across all test cases. Also, exploitation, exploration, parameter sensitivity analyses were performed have comprehensive understanding new algorithm. Additionally, was applied optimize parameters Proportional-Integral-Derivative (PID) controller for magnetic train positioning—a complex nonlinear control challenge. efficiently fine-tuned PID gains, enhancing system stability, precise positioning, improved response times. successful implementation underscores algorithm’s versatility effectiveness in handling real-world engineering problems. positive outcomes from extensive benchmarking practical application show FFO’s potential as powerful tool. In applying multi-objective optimization, demonstrated performance with sum mean errors 190.563, outperforming particle swarm (250.075) dynamic differential annealed (219.629). These results indicate ability achieve reliable tuning systems. Furthermore, achieved competitive large-scale its scalability robustness.
Language: Английский
Citations
1
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Feb. 21, 2025
This paper presents a hybrid approach that combines genetic algorithm (GA)-optimized type-2 fuzzy logic controller (T2FLC) with fractional-order technique for enhanced control of microgrid system. The T2FLC is employed to handle the inherent uncertainties in due fluctuating renewable energy inputs and varying loads. GA optimizes parameters designed FO-T2FLC approach, ensuring optimal performance under different operational conditions. developed strategy modification development traditional as it characterized by rapid dynamic response, high durability, distinctive performance, ease application, inexpensive. Also, this does not depend on mathematical model studied system, which gives satisfactory results if system change. direct current side features photovoltaic array battery storage. In contrast, alternating section comprises multi-functional voltage source inverter integrated shunt active power filter. setup delivers connected loads network. To manage effectively; methods (direct space vector modulation) are used section. Additionally, proposed regulator bus loop, regulate reactive loops network, compensate total harmonic distortion streams. It also injects required into network enhance competence work, efficiency FO-T2FLC-GA verified using MATLAB, comparing T2FLC-GA some existing strategies such third-order sliding mode control. obtained highlight effectiveness strength improving quality reducing value, reduces value percentages estimated at 80%, 33.87%, 32.50% all cases. steady-state error, undershoot, fluctuations, overshoot link compared 1.54%, 33.04%, 25%, respectively. Compared other works, improves response time, overshoot, ripples 59.38%, 50%, 75%, respectively, approach. These could make prominent solution future industrial applications propulsion traction.
Language: Английский
Citations
1
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: March 25, 2025
Abstract Microgrids integrate various distributed energy resources to enhance reliability and sustainability. Power electronic converters are vital in microgrids since they provide efficient, reliable, flexible operation. There numerous controllers available that can be applied these converters, lately, fractional-order (FOC) have gathered huge recognition. These enhanced flexibility superior performance managing dynamic behavior. structures of FOCs, this article predominantly focuses on comparing different cascaded fractional order (C-FOC). Four distinct topologies proportional integral (C-FOPI) selected for comparison with one another the controller used a non-minimum phase converter, such as boost converter employed microgrid system. The optimized using Elephant Herd Optimization (EHO) algorithm Integral Time-weighted Absolute Error (ITAE) serving metric. Each is subject variation system changes, outcomes documented correlated ascertain optimum structure. simulation endorsed notable advancements terms transient steady-state performance, featuring improved resilience parameter reduction 36.6% settling time, 15% overshoot, 20.1% rise an margin more than 51% 50% indices compared traditional (PI-PI).
Language: Английский
Citations
1
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 9, 2025
Language: Английский
Citations
0
Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown
Published: March 3, 2025
Language: Английский
Citations
0
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: March 4, 2025
Accurate regulation of the liquid level in a quadruple tank system (QTS) is not easy and imposes higher requirements on control strategies, so design controllers these systems challenging due to difficulty dynamic analysis its nonlinear characteristics parametric uncertainties. To overcome problems increase robustness pump coefficients, this article proposes investigates use an optimal hybrid fractional-order type-2 fuzzy-PID (OH-FO-T2F-PID) regulator using combination two bio-inspired evolutionary optimizers, namely augmented grey wolf optimizer cuckoo search optimizer, which gives rise new A-GWOCS algorithm. This mechanism was chosen facilitate convergence water liquids tanks as quickly possible corresponding required values. In addition, collaborative optimization technique with several objectives used adjust parameters. The capability efficiency suggested first investigated through computer simulation results then confirmed by real-time experimental QTS based dSPACE 1104 computation engine. findings showed that OH-FO-T2F-PID significantly outperformed both optimized ADRC OH-FO-T1F-PID regulators. Specifically, it reduced rising time 17.02% 95.21%, respectively, settling 25.13% 74.28%. Additionally, designed successfully eliminated steady-state error overshoot, enabling precise QTS, maintenance at desired set point under wide range working situations. recommended also studied considering - 50% disturbance parameters, less susceptible variations
Language: Английский
Citations
0
Sustainable Computing Informatics and Systems, Journal Year: 2025, Volume and Issue: unknown, P. 101120 - 101120
Published: March 1, 2025
Language: Английский
Citations
0
Deleted Journal, Journal Year: 2025, Volume and Issue: 7(5)
Published: April 25, 2025
Abstract Boiler system control presents significant challenges due to its complex, high-order dynamics, which make real-time computationally demanding. Traditional model order reduction (MOR) techniques often compromise accuracy, while conventional Proportional-Integral-Derivative (PID) tuning methods struggle with nonlinearities and dynamic uncertainties. This study proposes a dual-stage optimization framework that integrates balanced truncation-based nature-inspired metaheuristic algorithms for PID controller address these issues. The controllers are optimized using both classical such as Ziegler-Nichols (ZN), Simple Internal Model Control (SIMC), Approximate M-Constrained Integral Gain Optimization (AMIGO), Chien-Hrones-Reswick (CHR), well advanced like Particle Swarm (PSO), Krill Herd (KHO), Harris Hawks (HHO), Moth-Flame (MFO), Sparrow Search (SSO). Experimental results demonstrate the PSO-optimized achieves 20% in settling time 14.68% improvement Square Error (ISE) compared methods. HHO-based approach improves overall performance by 15%, SSO significantly reduces computational complexity 65% maintaining 98% accuracy. Statistical analysis (p < 0.05) confirms robustness of proposed methodology, showing 45% standard deviation traditional approaches. offers scalable, efficient, high-performance solution industrial boiler systems, ensuring improved stability, faster response times, adaptability over existing strategies.
Language: Английский
Citations
0