دورية أكاديمية

Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm

التفاصيل البيبلوغرافية
العنوان: Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm
المؤلفون: Dimitris Mourtzis, John Angelopoulos
المصدر: Machines, Vol 11, Iss 7, p 724 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Mechanical engineering and machinery
مصطلحات موضوعية: industry 5.0, optimization, power control, smart grid, particle swarm optimization, Mechanical engineering and machinery, TJ1-1570
الوصف: Climate change, improved energy efficiency, and access to contemporary energy services are among the key topics investigated globally. The effect of these transitions has been amplified by increased digitization and digitalization, as well as the establishment of reliable information and communication infrastructures, resulting in the creation of smart grids (SGs). A crucial aspect in optimizing energy production and distribution is reactive power optimization, which involves the utilization of algorithms such as particle swarm optimization (PSO). However, PSO algorithms can suffer from premature convergence and being trapped in local optima. Therefore, in this research the design and development of an improved PSO algorithm for minimization of power loss in the context of SGs is the key contribution. For digital experimentation and benchmarking of the proposed framework, the IEEE 30-bus standardized model is utilized, which has indicated that an improvement of approximately 11% compared to conventional PSO algorithms can be achieved.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2075-1702
Relation: https://www.mdpi.com/2075-1702/11/7/724; https://doaj.org/toc/2075-1702
DOI: 10.3390/machines11070724
URL الوصول: https://doaj.org/article/954585e011f14e2caeed43366d4798a6
رقم الأكسشن: edsdoj.954585e011f14e2caeed43366d4798a6
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20751702
DOI:10.3390/machines11070724