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

et al.

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(9), P. 532 - 532

Published: Sept. 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.

Language: Английский

Deterministic and probabilistic wind speed forecasting using decomposition methods: Accuracy and uncertainty DOI
Qian Sun, Jinxing Che, Kun Hu

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122515 - 122515

Published: Jan. 1, 2025

Language: Английский

Citations

1
Informer learning framework based on secondary decomposition for multi-step forecast of ultra-short term wind speed DOI

Zihao Jin,

Xiaomengting Fu,

Ling Xiang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 139, P. 109702 - 109702

Published: Nov. 22, 2024

Language: Английский

Citations

4
A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model DOI Creative Commons

Jie Du,

S. C. Chen,

Linlin Pan

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1136 - 1136

Published: Feb. 25, 2025

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of power resources. However, sequences often exhibit complex characteristics such as instability volatility, which create substantial challenges for prediction. In order to cope with these challenges, multi-step method based on secondary decomposition (SD) techniques deep learning models is proposed this paper. First, original signal was decomposed into multiple by using two techniques, multi-scale wavelet spectrum analysis (MWPSA) variational mode (VMD). Second, model constructed combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, attention mechanism perform predicting each sequence, parameters were optimized particle swarm optimization (PSO) algorithm. Ultimately, results from all combined generate final The predictive performance evaluated real data collected farm China. Experimental show that significantly outperforms other comparison prediction, highlights its accuracy reliability.

Language: Английский

Citations

0
Interpretable wind speed forecasting through two-stage decomposition with comprehensive relative importance analysis DOI
Huanze Zeng, Binrong Wu, Haoyu Fang

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 392, P. 126015 - 126015

Published: May 5, 2025

Language: Английский

Citations

0
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

et al.

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(9), P. 532 - 532

Published: Sept. 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.

Language: Английский

Citations

1