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1. Adaptation of the Simulated Evolution Algorithm for Wind Farm Layout Optimization.

Author

Khan, Salman A.

Subjects
WIND power plants, OFFSHORE wind power plants, PARTICLE swarm optimization, WIND power, ALGORITHMS, GENETIC algorithms, NP-hard problems
Abstract

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2022
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2. Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm.

Author

Alturki, Fahd A., Al‐Shamma'a, Abdullrahman A., Farh, Hassan M. H., and AlSharabi, Khalil

Subjects
MATHEMATICAL optimization, ENERGY consumption, ALGORITHMS, PARTICLE swarm optimization, COMPUTATIONAL intelligence, AUTONOMOUS differential equations, HYBRID systems
Abstract

Summary: Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply‐demand‐based optimization (SDO) is applied to the HES sizing problem based on long‐term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big‐bang‐big‐crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.7 kW from PV arrays, 231.6 kW from WTs, 192.5 kWh from battery banks, and 267.6 kW from the DGS. The optimal HES exhibits a high REF (66.4%) and is economically feasible ($104 323.10/year) and environmentally friendly. The entire load demand of the area under study is met without power loss (LPSP = 0%). [ABSTRACT FROM AUTHOR]

Published
2021
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3. New Heart Disease Findings from Prince Sattam Bin Abdulaziz University Reported (Quantum-enhanced Machine Learning Algorithms for Heart Disease Prediction).

Subjects
MACHINE learning, HEART diseases, PARTICLE swarm optimization, QUANTUM computing, PRINCES
Abstract

Researchers from Prince Sattam Bin Abdulaziz University in Al Kharj, Saudi Arabia, have developed a quantum-enhanced machine learning algorithm for predicting heart disease. The algorithm, called quantum ML with quantum particle swarm optimization (QPSO), was compared to a traditional machine learning approach called multilayer perceptron (MLP). The QML with QPSO algorithm achieved an accuracy of 96.7% and a false detection rate of 0.09, outperforming the MLP approach. This research highlights the potential of quantum computing in improving the accuracy of heart disease prediction. [Extracted from the article]

Published
2023