PARÇALI GÖLGELENME DURUMUNDA YAPAY SİNİR AĞLARI VE PARÇACIK SÜRÜ OPTİMİZASYONU TABANLI BİR MAKSİMUM GÜÇ NOKTASI TAKİBİ ALGORİTMASI

Elif Baldan; Hüseyin Erişti

doi:10.17780/ksujes.1318480

EN TR

A MAXIMUM POWER POINT TRACKING ALGORITHM BASED ON ARTIFICAL NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION IN PARTAL SHADING

Abstract

In photovoltaic (PV) systems, partial shading occurs under real conditions when maximum power point tracking (MPPT) is performed. In this paper, a PV system consisting of PV panels and a boost converter is created in MATLAB/Simulink to investigate the partial shadowing conditions. Conventional and artificial intelligence-based MGNT algorithms are applied to this system. In order to track the maximum power point (MPP), the traditional method of the Perturb and Observe algorithm and Artificial Neural Networks (ANN) technique are used. In addition to the classical ANN technique, a hybrid technique was created with Particle Swarm Optimization (PSO). First, the partial shading situation was simulated with different scenarios. To support the accuracy of the algorithms, real-time irradiance data for two days, both sunny and cloudy, were collected and analyzed in MATLAB/Simulink on the PV system. As a result of the analysis, it was observed that the PSO-based ANN technique tracks MPP more efficiently than other algorithms. This study contributes to the studies on MGNT in the case of partial shading and demonstrates the use of artificial intelligence algorithms for PV systems, which is a different field.

Keywords

PARÇALI GÖLGELENME DURUMUNDA YAPAY SİNİR AĞLARI VE PARÇACIK SÜRÜ OPTİMİZASYONU TABANLI BİR MAKSİMUM GÜÇ NOKTASI TAKİBİ ALGORİTMASI

Öz

Fotovoltaik (photovoltaic - PV) sistemlerde maksimum güç noktası takibi (MGNT) yapılırken gerçek koşullarda parçalı gölgelenme durumu oluşmaktadır. Bu makalede parçalı gölgelenme koşullarını incelemek için MATLAB/Simulink’te PV paneller ve yükseltici dönüştürücüden oluşan bir PV sistem oluşturulmuştur. Geleneksel ve yapay zeka tabanlı MGNT algoritmaları bu sistem üzerinde uygulanmıştır. Maksimum güç noktasını (MGN) takip etmek için geleneksel yöntem olan Değiştir ve Gözle algoritması ve Yapay Sinir Ağları (YSA) tekniği kullanılmıştır. Klasik YSA tekniğinin yanısıra Parçacık Sürü Optimizasyonu (PSO) ile hibrit bir teknik oluşturulmuştur Farklı senaryolar ile ilk olarak parçalı gölgelenme durumu simulasyon olarak oluşturulmuştur. Algoritmaların doğruluğunu desteklemek için hem güneşli hem de bulutlu olmak üzere iki güne ait gerçek zamanlı ışınım verileri toplanarak MATLAB/Simulink’te oluşturulan PV sistemde analizler yapılmıştır. Yapılan analizler sonucunda PSO tabanlı YSA tekniği diğer algoritmalara göre daha verimli bir şekilde MGN’yi izlediği gözlemlenmiştir. Bu çalışma ile parçalı gölgelenme durumunda MGNT üzerine yapılan çalışmalara katkı sağlanmaktadır ve yapay zeka algoritmalarının farklı bir alan olan PV sistemler için kullanımı gösterilmiştir.

Anahtar Kelimeler

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Details

Primary Language

Turkish

Subjects

Photovoltaic Power Systems , Electrical Engineering (Other)

Journal Section

Research Article

Authors

Elif Baldan ^*
0009-0007-1248-4064
Türkiye

Hüseyin Erişti
0000-0003-1474-9170
Türkiye

Publication Date

December 3, 2023

Submission Date

June 22, 2023

Acceptance Date

October 23, 2023

Published in Issue

Year 2023 Volume: 26 Number: 4

DOI

https://doi.org/10.17780/ksujes.1318480

IZ

https://izlik.org/JA44NR25HA

Cite

RIS / Bibtex

APA

Baldan, E., & Erişti, H. (2023). PARÇALI GÖLGELENME DURUMUNDA YAPAY SİNİR AĞLARI VE PARÇACIK SÜRÜ OPTİMİZASYONU TABANLI BİR MAKSİMUM GÜÇ NOKTASI TAKİBİ ALGORİTMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(4), 895-908. https://doi.org/10.17780/ksujes.1318480

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