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Optimal Performance of Dynamic Particle Swarm Optimization Based Maximum Power Trackers for Stand-Alone PV System Under Partial Shading Conditions

الأداء الأمثل لمتعقبات الطاقة القصوى القائمة على تحسين سرب الجسيمات الديناميكية للنظام الكهروضوئي المستقل في ظل ظروف التظليل الجزئي
Authors: Sergey Obukhov; Ahmed Ibrahim; Ahmed A. Zaki Diab; Ameena Saad Al-Sumaiti; Raef Aboelsaud;

Optimal Performance of Dynamic Particle Swarm Optimization Based Maximum Power Trackers for Stand-Alone PV System Under Partial Shading Conditions

Abstract

Una de las tareas importantes para aumentar la eficiencia del sistema fotovoltaico (PV) es el desarrollo y la mejora de los algoritmos de seguimiento del punto de máxima potencia (MPPT). Estos algoritmos MPPT conducen a la capacidad de capturar de manera eficiente el punto de máxima potencia global de la matriz fotovoltaica parcialmente sombreada. Uno de estos rastreadores es el algoritmo de optimización de enjambre de partículas (PSO), que es una de las técnicas de computación blanda. Los rastreadores convencionales basados en PSO tienen muchas ventajas, como la simplicidad de la implementación de hardware y la independencia del sistema instalado. El problema real de la aplicación práctica del PSO es la determinación de sus parámetros para garantizar una alta efectividad de la extracción del MPP global. El análisis de los trabajos científicos dedicados al algoritmo PSO ha demostrado que actualmente no existe una metodología para la selección de los parámetros óptimos de los seguidores de máxima potencia basados en el algoritmo PSO para el sistema fotovoltaico. Este trabajo tiene como objetivo crear un método conveniente y razonable para elegir los parámetros óptimos del algoritmo PSO, teniendo en cuenta la topología y los parámetros del convertidor DC-DC y la configuración de los paneles solares. Se ha presentado un nuevo método para seleccionar los parámetros de un convertidor reductor conectado a una batería. El valor óptimo del tiempo de muestreo para los controladores digitales MPP, proporcionando su máximo rendimiento; se ha determinado en base a una nueva metodología. El paquete de software Matlab/Simulink se utiliza como la principal herramienta de investigación. Los resultados destacados identifican que el PSO modificado y sus parámetros diseñados cumplen mejor con los requisitos del controlador MPPT para el sistema fotovoltaico.

L'une des tâches importantes pour augmenter l'efficacité du système photovoltaïque (PV) est le développement et l'amélioration des algorithmes de suivi du point de puissance maximale (MPPT). Ces algorithmes MPPT permettent de capturer efficacement le point de puissance maximale global du réseau photovoltaïque partiellement ombré. L'un de ces trackers est l'algorithme d'optimisation d'essaim de particules (PSO) qui est l'une des techniques de calcul logiciel. Les trackers conventionnels basés sur PSO présentent de nombreux avantages tels que la simplicité de mise en œuvre du matériel et l'indépendance par rapport au système installé. Le véritable problème de l'application pratique de PSO est la détermination de ses paramètres pour assurer une grande efficacité d'extraction du MPP mondial. L'analyse des articles scientifiques consacrés à l'algorithme PSO a montré qu'il n'existe actuellement aucune méthodologie pour la sélection des paramètres optimaux des suiveurs de puissance maximale basés sur l'algorithme PSO pour le système PV. Cet article vise à créer une méthode pratique et raisonnable pour choisir les paramètres optimaux de l'algorithme PSO, en tenant compte de la topologie et des paramètres du convertisseur DC-DC et de la configuration des panneaux solaires. Une nouvelle méthode de sélection des paramètres d'un convertisseur abaisseur de tension connecté à une batterie a été présentée. La valeur optimale du temps d'échantillonnage pour les contrôleurs MPP numériques, fournissant leurs performances maximales ; a été déterminée sur la base d'une nouvelle méthodologie. Le progiciel Matlab/Simulink est utilisé comme principal outil de recherche. Les principaux résultats identifient que le PSO modifié et ses paramètres conçus répondent le mieux aux exigences du contrôleur MPPT pour le système PV.

One of the important tasks for increasing the efficiency of photovoltaic (PV) system is the development and improvement of the maximum power point tracking algorithms (MPPT). These MPPT algorithms lead to the ability to catch efficiently the global maximum power point of the partially shaded PV array. One of these trackers is the particle swarm optimization (PSO) algorithm which is one of the Soft computing techniques. The conventional PSO based trackers have many advantages such as the simplicity of hardware implementation and independence from the installed system. The actual problem of the practical application of PSO is the determination of its parameters to ensure high effectiveness of extracting the global MPP. Analysis of scientific papers devoted to the PSO algorithm has shown that there is currently no methodology for the optimal parameters' selection of PSO algorithm based maximum power trackers for the PV system. This paper aims to create a convenient and reasonable method for choosing the optimal parameters of the PSO algorithm, taking into account the topology and parameters of the DC-DC converter and the configuration of solar panels. A new method for selecting the parameters of a buck converter connected to a battery has been presented. The optimal value of the sampling time for the digital MPP controllers, providing their maximum performance; has been determined based on a new methodology. Matlab/Simulink software package is used as the main research tool. The prominent outcomes identify that the modified PSO and its designed parameters best meet the requirements of the MPPT controller for the PV system.

تتمثل إحدى المهام المهمة لزيادة كفاءة النظام الكهروضوئي (PV) في تطوير وتحسين خوارزميات تتبع نقطة الطاقة القصوى (MPPT). تؤدي خوارزميات MPPT هذه إلى القدرة على التقاط نقطة الطاقة القصوى العالمية للمصفوفة الكهروضوئية المظللة جزئيًا بكفاءة. أحد أجهزة التتبع هذه هي خوارزمية تحسين سرب الجسيمات (PSO) التي تعد واحدة من تقنيات الحوسبة اللينة. تتمتع أجهزة التتبع التقليدية التي تعتمد على PSO بالعديد من المزايا مثل بساطة تنفيذ الأجهزة والاستقلال عن النظام المثبت. تتمثل المشكلة الفعلية للتطبيق العملي لـ PSO في تحديد معاييره لضمان فعالية عالية لاستخراج MPP العالمي. أظهر تحليل الأوراق العلمية المخصصة لخوارزمية PSO أنه لا توجد حاليًا منهجية لاختيار المعلمات المثلى لأجهزة تتبع الطاقة القصوى القائمة على خوارزمية PSO للنظام الكهروضوئي. تهدف هذه الورقة إلى إنشاء طريقة مريحة ومعقولة لاختيار المعلمات المثلى لخوارزمية PSO، مع مراعاة طوبولوجيا ومعلمات محول DC - DC وتكوين الألواح الشمسية. تم تقديم طريقة جديدة لاختيار معلمات محول باك المتصل بالبطارية. تم تحديد القيمة المثلى لوقت أخذ العينات لوحدات تحكم MPP الرقمية، مما يوفر أقصى أداء لها ؛ بناءً على منهجية جديدة. تُستخدم حزمة برامج Matlab/Simulink كأداة بحث رئيسية. تحدد النتائج البارزة أن PSO المعدل ومعلماته المصممة تلبي على أفضل وجه متطلبات وحدة تحكم MPPT للنظام الكهروضوئي.

Country
Russian Federation
Keywords

Artificial intelligence, PV, Spectral Beam Splitting, Engineering, Inverter, Photovoltaic system, Maximum Power Point Tracking, Soft computing, Energy, Particle swarm optimization, Physics, Mathematical optimization, PSO, Power (physics), Algorithm, Physical Sciences, battery, Electrical engineering. Electronics. Nuclear engineering, buck converter, MATLAB, partial shading, batteries, MPPT Techniques, PV System, MPPT, Partial Shading, Control (management), Quantum mechanics, resistance, Artificial Intelligence, FOS: Electrical engineering, electronic engineering, information engineering, Control theory (sociology), FOS: Mathematics, Machine Learning Methods for Solar Radiation Forecasting, Maximum power principle, Multijunction Solar Cell Technology, Electrical and Electronic Engineering, Biology, Renewable Energy, Sustainability and the Environment, voltage control, Controller (irrigation), maximum power point trackers, Photovoltaic Maximum Power Point Tracking Techniques, Computer science, Maximum power point tracking, Agronomy, TK1-9971, Fuzzy logic, Operating system, Electrical engineering, Computer Science, photovoltaic systems, buck converters, mathematical model, Mathematics

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
117
Top 1%
Top 10%
Top 1%
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