Leveraging the Performance of Integrated Power Systems with Wind Uncertainty Using Fractional Computing-Based Hybrid Method DOI Creative Commons
Hani Albalawi, Yasir Muhammad, Abdul Wadood

и другие.

Fractal and Fractional, Год журнала: 2024, Номер 8(9), С. 532 - 532

Опубликована: Сен. 11, 2024

Reactive power dispatch (RPD) in electric systems, integrated with renewable energy sources, is gaining popularity among engineers because of its vital importance the planning, designing, and operation advanced systems. The goal RPD to upgrade system performance by minimizing transmission line losses, enhancing voltage profiles, reducing total operating costs tuning decision variables such as transformer tap setting, generator’s terminal voltages, capacitor size. But complex, non-linear, dynamic characteristics networks, well presence demand uncertainties non-stationary behavior wind generation, pose a challenging problem that cannot be solved efficiently traditional numerical techniques. In this study, new fractional computing strategy, namely, hybrid particle swarm optimization (FHPSO), proposed handle issues networks plants (WPPs) while incorporating uncertainties. To improve convergence Particle Swarm Optimization Gravitational Search Algorithm (PSOGSA), FHPSO incorporates concepts Shannon entropy inside mathematical model PSOGSA. Extensive experimentation validates effectiveness best value objective functions, deviation index loss minimization standard shows an improvement percentage 61.62%, 85.44%, 86.51%, 93.15%, 84.37%, 67.31%, 61.64%, 61.13%, 8.44%, 1.899%, respectively, over ALC_PSO, FAHLCPSO, OGSA, ABC, SGA, CKHA, NGBWCA, KHA, PSOGSA, FPSOGSA case optimal reactive dispatch(ORPD) for IEEE 30 bus system. Furthermore, stability, robustness, precision designed are determined using statistical interpretations cumulative distribution function graphs, quantile-quantile plots, boxplot illustrations, histograms.

Язык: Английский

RÜZGÂR GÜCÜ TAHMİNİNDE UZUN KISA-SÜRELİ BELLEK: VERİ ÖRNEKLEME VE KÜMELEMENİN ETKİSİ DOI Open Access
Volkan Yamaçlı

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, Год журнала: 2025, Номер 28(1), С. 202 - 215

Опубликована: Март 3, 2025

Rüzgâr enerjisi, temiz, yenilenebilir ve çevre dostu olarak geleneksel güç kaynaklarının en verimli alternatiflerinden biridir. Bununla birlikte, rüzgâr hızının dolayısıyla kalitesinin değişken doğasından dolayı, elektrik şebekesinin güvenliği güvenilirliğinin önünde bazı engeller oluşabilmektedir. hızı gücü tahmini aracılığı ile planlaması sorununu çözebilmek için, popüler yinelemeli sinir ağlarından (YNSA) biri olan uzun kısa-süreli bellek (UKSB) tabanlı bir tahmin modeli önerilmektedir. Bu çalışmada Türkiye’de mevcut türbininden elde edilen yayımlanan veri seti kullanılmıştır. İlk UKSB ağı, zaman-dizilerine ilişkin farklı pencere boyutundaki veriler için eğitilmiştir. Daha sonra bu iki ağının çıktıları başka ağı girdi kullanılarak daha yüksek aralıklarla az miktarda sağlam yaklaşım sağlanması hedeflenmiştir. Nihai verileri, her dizinin sonuçları edilir. 30-dakikalık, 1-saatik, 6-saatlik 1-günlük 4 durum çalışması yapılarak önerilen algoritmanın etkinliği gösterilmiştir.

Процитировано

0
Colonial bacterial memetic algorithm and its application on a darts playing robot DOI Creative Commons
Szilárd Kovács, Csaba Budai, János Botzheim

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 28, 2025

In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CBMA extends by integrating Cultural Algorithms and co-evolutionary dynamics inspired bacterial group behavior. This combination of natural artificial elements results in a robust algorithm capable handling complex challenges robotics, such as constraints, multiple objectives, large search spaces, models, while delivering fast accurate solutions. incorporates features like multi-level clustering, dynamic gene selection, hierarchical population adaptive mechanisms, enabling efficient management task-specific parameters optimizing solution quality minimizing resource consumption. The algorithm's effectiveness is demonstrated through real-world application, achieving 100% success rate robot arm's ball-throwing task usually with significantly fewer iterations evaluations compared to other methods. was also evaluated using CEC-2017 benchmark suite, where it consistently outperformed state-of-the-art algorithms, superior outcomes 71% high-dimensional cases demonstrating up 80% reduction required evaluations. These highlight CBMA's efficiency, adaptability, suitability specialized tasks. Overall, exhibits exceptional performance both evaluations, effectively balancing exploration exploitation, representing significant advancement robotics.

Язык: Английский

Процитировано

0
Optimal Power Flow in Hybrid Wind-PV-V2G Systems with Dynamic Load Demand using a Hybrid MRFO-AHA Algorithm DOI Creative Commons
Mohamed H. Hassan, Salah Kamel, Ayoob Alateeq

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 174297 - 174329

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

2
Leveraging the Performance of Integrated Power Systems with Wind Uncertainty Using Fractional Computing-Based Hybrid Method DOI Creative Commons
Hani Albalawi, Yasir Muhammad, Abdul Wadood

и другие.

Fractal and Fractional, Год журнала: 2024, Номер 8(9), С. 532 - 532

Опубликована: Сен. 11, 2024

Reactive power dispatch (RPD) in electric systems, integrated with renewable energy sources, is gaining popularity among engineers because of its vital importance the planning, designing, and operation advanced systems. The goal RPD to upgrade system performance by minimizing transmission line losses, enhancing voltage profiles, reducing total operating costs tuning decision variables such as transformer tap setting, generator’s terminal voltages, capacitor size. But complex, non-linear, dynamic characteristics networks, well presence demand uncertainties non-stationary behavior wind generation, pose a challenging problem that cannot be solved efficiently traditional numerical techniques. In this study, new fractional computing strategy, namely, hybrid particle swarm optimization (FHPSO), proposed handle issues networks plants (WPPs) while incorporating uncertainties. To improve convergence Particle Swarm Optimization Gravitational Search Algorithm (PSOGSA), FHPSO incorporates concepts Shannon entropy inside mathematical model PSOGSA. Extensive experimentation validates effectiveness best value objective functions, deviation index loss minimization standard shows an improvement percentage 61.62%, 85.44%, 86.51%, 93.15%, 84.37%, 67.31%, 61.64%, 61.13%, 8.44%, 1.899%, respectively, over ALC_PSO, FAHLCPSO, OGSA, ABC, SGA, CKHA, NGBWCA, KHA, PSOGSA, FPSOGSA case optimal reactive dispatch(ORPD) for IEEE 30 bus system. Furthermore, stability, robustness, precision designed are determined using statistical interpretations cumulative distribution function graphs, quantile-quantile plots, boxplot illustrations, histograms.

Язык: Английский

Процитировано

1