Elman Neural Network with Customized Particle Swarm Optimization for Hydraulic Pitch Control Strategy of Offshore Wind Turbine DOI Open Access

Valayapathy Lakshmi Narayanan,

Jyotindra Narayan, Dheeraj Kumar Dhaked

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(3), P. 808 - 808

Published: March 10, 2025

Offshore wind turbines have garnered significant attention recently due to their substantial energy harvesting capabilities. Pitch control plays a crucial role in maintaining the rated generator speed, particularly offshore environments characterized by highly turbulent winds, which pose huge challenge. Moreover, hydraulic pitch systems are favored large-scale superior power-to-weight ratio compared electrical systems. In this study, proportional valve-controlled system is developed along with an intelligent strategy aimed at developing power turbines. The proposed utilizes cascade configuration of improved recurrent Elman neural network, its parameters optimized using customized particle swarm optimization algorithm. To assess effectiveness, two other strategies, network and tested benchmark turbine simulator. Results demonstrate effective generation, yielding 78.14% 87.10% enhancement mean standard deviation error respectively. These findings underscore efficacy approach generating power.

Language: Английский

A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions DOI Creative Commons
Ibrahim Alhamrouni, Nor Hidayah Abdul Kahar,

Mohaned Salem

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6214 - 6214

Published: July 17, 2024

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase renewable sources become more integrated, maintaining stability reliability both conventional systems smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s grids’ complex, dynamic nature. paper discusses adoption advanced intelligence methods, including artificial (AI), deep learning (DL), machine (ML), metaheuristic optimization algorithms, other AI such as fuzzy logic, reinforcement learning, model predictive control address these challenges. It underscores critical importance system new challenges integrating diverse sources. The reviews various used in analysis, emphasizing their roles maintenance, fault detection, real-time monitoring. details extensive research on capabilities ML algorithms precision efficiency protection systems, showing effectiveness accurately identifying resolving faults. Additionally, it explores potential logic decision-making under uncertainty, integration IoT big data analytics monitoring optimization. Case studies from literature presented, offering valuable insights into practical applications. concludes by current limitations suggesting areas future research, highlighting need robust, flexible, scalable sector. a resource researchers, engineers, policymakers, providing detailed understanding

Language: Английский

Citations

21
Optimal sizing of off-grid microgrid Building-Integrated-Photovoltaic system with battery for a Net Zero Energy Residential Building in different climates of Morocco DOI Creative Commons

Sarah Forrousso,

Samir Idrissi Kaitouni,

Abdelali Mana

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102288 - 102288

Published: May 21, 2024

An optimal sizing of an off-grid microgrid system composed photovoltaic (PV)/building integrated (BIPV)/battery energy storage installation is undergone for Net Zero Energy Residential Building blocks across six different climates Morocco in order to reach the objective providing all load requirements at minimum. The Particle Swarm Optimization algorithm used find system, by considering hourly spatiotemporal variations both solar availability and demand variation, with lowest Total Annualized Cost as function capacities BIPV battery decision variables. methodology adopted focuses on main fulfillment through direct PV power supply, backed technology, continually guarantee self-sufficiency. A key metric, cover factor, introduced quantify ratio which satisfied systems. findings show that can help improve factor 0.68-2.58%. Moreover, integrating Battery leads a reduction Levelized approximately 8.7-20.72 %, opposed utilizing only battery. Depending local climate, levelized cost ranges from 0.366 $/kWh Ouarzazate city up 0.664 $/kWh.in Ifrane city. Lastly, this holistic approach aims transform building its traditional role consumer carbon-free electricity generator.

Language: Английский

Citations

20
Towards Renewables Development: Review of Optimization Techniques for Energy Storage and Hybrid Renewable Energy Systems DOI Creative Commons

Oluwatoyosi Bamisile,

Dongsheng Cai, Humphrey Adun

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(19), P. e37482 - e37482

Published: Sept. 10, 2024

As global energy demand and warming increase, there is a need to transition sustainable renewable sources. Integrating different systems create hybrid system enhances the overall adoption deployment of resources. Given intermittent nature solar wind, storage are combined with these sources, optimize quantity clean used. Thus, various optimization strategies have been developed for integration operation systems. Existing studies either reviewed or systems, however, ignored integrated This study offers comprehensive analysis methods used in (HRES) (ESS). We examined models HRES ESS, their objectives, common constraints. Based on our review, capacity CO

Language: Английский

Citations

16
The Role of Smart Grid Technologies in Urban and Sustainable Energy Planning DOI Creative Commons
Mohamed G Moh Almihat, Josiah L. Munda

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1618 - 1618

Published: March 24, 2025

Traditional centralized energy grids struggle to meet urban areas’ increasingly complex demands, necessitating the development of more sustainable and resilient solutions. Smart microgrids offer a decentralized approach that enhances efficiency, facilitates integration renewable sources, improves resilience. This study follows systematic review approach, analyzing literature published in peer-reviewed journals, conference proceedings, industry reports between 2011 2025. The research draws from academic publications institutions alongside regulatory reports, examining actual smart microgrid deployments San Diego, Barcelona, Seoul. Additionally, this article provides real-world case studies New York London, showcasing successful unsuccessful deployments. Brooklyn Microgrid demonstrates peer-to-peer trading, while London faces regulations funding challenges its systems. paper also explores economic policy frameworks such as public–private partnerships (PPPs), localized markets, standardized models enable adoption at scale. While PPPs provide financial infrastructural support for deployment, they introduce stakeholder alignment compliance complexities. Countries like Germany India have successfully used development, leveraging low-interest loans, government incentives, mechanisms encourage innovation technologies. In addition, examines new trends utilization AI quantum computing optimize energy, climate design before outlining future agenda focused on cybersecurity, decarbonization, inclusion technology. Contributions include modular scalable framework, innovative hybrid storage systems, performance-based model suited environment. These contributions help fill gap what is possible today needed systems create foundation cities next century.

Language: Английский

Citations

1
Simultaneous community energy supply-demand optimization by microgrid operation scheduling optimization and occupant-oriented flexible energy-use regulation DOI
Chengyu Zhang, Yacine Rezgui, Zhiwen Luo

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 373, P. 123922 - 123922

Published: July 17, 2024

Language: Английский

Citations

7
Fuzzy Logic-Based Particle Swarm Optimization for Integrated Energy Management System Considering Battery Storage Degradation DOI Creative Commons
Oladimeji Ibrahim, Mohd Junaidi Abdul Aziz, Razman Ayop

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 102816 - 102816

Published: Aug. 30, 2024

Considering the rapidly evolving microgrid technology and increasing complexity associated with integrating renewable energy sources, innovative approaches to management are crucial for ensuring sustainability efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Optimization (FLB-PSO) technique enhance performance of hybrid systems. The proposed FLB-PSO algorithm effectively addresses challenge balancing exploration exploitation in optimization problems, thereby enhancing convergence speed solution accuracy robustness across diverse complex scenarios. By leveraging adaptability fuzzy logic adjust PSO parameters dynamically, method optimizes allocation utilization resources within grid-connected microgrid. Under fixed grid tariffs, investigation demonstrates that achieves power purchase battery degradation costs $1935.07 $49.93, respectively, compared $2159.67 $61.43 traditional PSO. results an optimal cost $1985.00 FLB-PSO, leading saving $236.09 $2221.10 Furthermore, under dynamic incurs $2359.20 $64.66, contrast $2606.47 $54.61 is $2423.86, representing reduction $237.23 $2661.08 proficiently manages sources while addressing complexities storage degradation. Overall, outperforms terms system dynamics, rate, operational reduction, improved

Language: Английский

Citations

7
Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments DOI Creative Commons

Assala Bouguerra,

Abd Essalam Badoud, Saad Mekhilef

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 17, 2024

Abstract This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo (CS) algorithms adapting varying conditions, including fluctuations pressure temperature. Through meticulous simulations analyses, explores collaborative integration these techniques with boost converters enhance reliability productivity. It was found FSSO consistently works better than CS, achieving an average increase 12.5% power extraction from PEM a variety operational situations. Additionally, exhibits superior adaptability convergence speed, maximum point (MPP) 25% faster CS. These findings underscore substantial potential as robust efficient MPPT method for optimizing cell systems. The contributes quantitative insights advancing green energy solutions suggests avenues future exploration hybrid optimization

Language: Английский

Citations

6
Improved machine learning-based pitch controller for rated power generation in large-scale wind turbine DOI

Valayapathy Lakshmi Narayanan,

Dheeraj Kumar Dhaked,

R. Sitharthan

et al.

Renewable energy focus, Journal Year: 2024, Volume and Issue: 50, P. 100603 - 100603

Published: July 26, 2024

Language: Английский

Citations

5
Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services DOI Creative Commons
Gabriel Ioan Arcas, Tudor Cioara, Ionuț Anghel

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(5), P. 302 - 302

Published: May 18, 2024

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with transmission of large volumes data affecting latency control services and secure delivery energy. Offloading computational work towards edge is a viable option; however, effectively coordinating service execution on nodes presents significant due to vast search space making it difficult identify optimal decisions within limited timeframe. In this research paper, we utilize whale optimization algorithm decide select for executing services’ tasks. We employ directed acyclic graph model dependencies among nodes, network links, assets, organization, thereby facilitating more efficient navigation decision solution. The offloading variables are represented as binary vector, which evaluated using fitness function considering round-trip time correlation between edge-task resources. To explore strategies prevent convergence suboptimal solutions, adapt feedback mechanisms, an inertia weight coefficient, nonlinear factor. evaluation results promising, demonstrating that proposed solution can consider both constraints while enduring faster decision-making optimization, notable improvements in response low average approximately 0.03 s per iteration. Additionally, complex infrastructures modeled, our shows strong features terms diversity, evolution, time.

Language: Английский

Citations

4
Smart Grid Stability Prediction Using Adaptive Aquila Optimizer and Ensemble Stacked BiLSTM DOI Creative Commons
Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103261 - 103261

Published: Oct. 1, 2024

Language: Английский

Citations

4