Müstakil çalışan PV Sistem için Yükselten Tip Dönüştürücü topolojisine sahip Yapay Sinir Ağı tabanlı MPPT Algoritması

Year 2022, Volume: 15 Issue: 1, 242 - 257, 27.03.2022

Mehmet Yılmaz , Muhammedfatih Corapsiz

https://doi.org/10.18185/erzifbed.1002823

Cited By: 1

Abstract

Teknolojinin gelişmesine paralel olarak artan enerji ihtiyacı ve kaynakların tükenmesi, alternatif enerji kaynaklarının önemini artırmıştır. Güneş enerjisi sistemleri hareketli parça olmaması, güvenilir olması ve gürültüsüz çalışması gibi avantajları nedeniyle sıklıkla tercih edilmektedir. Güneş enerjisinden elektrik üretimi, istenilen gerilim ve akım değerlerine bağlı olarak fotovoltaik (PV) panellerin seri veya paralel bağlanması ile elde edilmektedir. PV panellerden elde edilen enerjiyi arzu edilen şebeke değerlerine dönüştürmek amacıyla DC-DC dönüştürücüler kullanılmaktadır. PV panellerden mümkün olan en yüksek verimi elde etmek için maksimum güç noktası izleme (MPPT) algoritmaları kullanılmaktadır. MPPT algoritmaları DC-DC dönüştürücülerin görev periyodu (D) oranını kontrol edip maksimum enerji elde etmektedirler. Bu çalışmada, Yapay Sinir Ağı (YSA) tabanlı bir MPPT algoritması önerilmiştir İlk olarak PV panel girişindeki sıcaklık ve ışınım verileri Levenberg-Marquardt algoritması kullanılarak eğitilmiştir Sonuç olarak, bir referans voltajı üretilir ve PV panel tarafından üretilen voltaj ile karşılaştırılarak MPPT gerçekleştirilmektedir. Önerilen algoritmanın performansını değerlendirmek için geleneksel MPPT yöntemlerinden Değiştir & Gözle (P&O) ve Artırılmış iletkenlik (INC) ile karşılaştırılmıştır. Benzetim çalışmaları sonucunda YSA tabanlı MPPT’nin çeşitli ışınım ve sıcaklık koşulları için P&O ve INC algoritmalarından daha başarılı olduğu gözlemlenmiştir.

Keywords

Yapay sinir ağı, maksimum güç noktası takibi, DC-DC dönüştürücü, artırılmış iletkenlik algoritması, değiştir & gözle algoritması

Artificial Neural Network based MPPT Algorithm with Boost Converter topology for Stand-Alone PV System

Abstract

The increasing energy need in parallel with the technology development and the depletion of the resources have increased the importance of alternative energy resources. Solar energy systems are frequently preferred due to their advantages such as not having moving parts, being reliable and working without noise. Production of electricity from solar energy is obtained by serial or parallel connection of photovoltaic (PV) panels, depending on the desired voltage and current values. DC-DC converters are used to convert the energy obtained from the PV panels to the desired grid values. Maximum power point tracking (MPPT) algorithms are used in order to obtain the highest possible efficiency from the PV panels. MPPT algorithms control the duty period (D) ratio of DC-DC converters and obtain maximum energy. In this study, an Artificial Neural Network (ANN) based MPPT algorithm is proposed. Firstly, the temperature and irradiance data at the PV panel input are trained using the Levenberg-Marquardt algorithm. As a result, a reference voltage is generated and MPPT is realized by comparing it with the voltage produced by the PV panel. In order to evaluate the performance of the proposed algorithm, it is compared with the traditional MPPT methods such as Perturb & Observe (P&O) and Incremental Conductance (INC). As a result of the simulation studies, it has been observed that ANN based MPPT is more successful than P&O and INC algorithms for several irradiance and temperature conditions.

Keywords

Artificial neural network, maximum power point tracking, DC-DC converter, incremental conductance algorithm, perturb & observe algorithm

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