An Overview of ANN based MPPT and an Example

Mehdi Rezaee; Yusuf Gürcan Şahin

Article Details

An Overview of ANN based MPPT and an Example

Authors:

Mehdi Rezaee | Yusuf Gürcan Şahin

Publisher:

İstanbul Gelisim University

Main Topics:

Fotovoltaik Güç Sistemleri

Journal:

International Journal of Engineering Technologies Vol.10 Issue 1, pp.9-22

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001347930

pISSN:

2149-0104

Keywords:

Abstract:

 The study presents an overview and a simulation of maximum power point tracking MPPT for Photovoltaic PV systems that uses an artificial neural network ANN controller as proof of concept. Solar energy must be harvested with high efficien-cy as the world turns to renewables. The usual Perturb and Observe PO and Incremental InC method loses power by oscil-lating around the Maximum Power Point MPP and reacts slowly to sudden weather changes. The work therefore tests an ANN as a better choice. The authors survey earlier ANN MPPT studies that cover many network types, training schemes and mixed strategies. They then build a MATLABSimulink model that runs an ANN controller and a PO controller on the same PV array. The ANN learns from Istanbul 2020 weather data. The results show the ANN reaches 252 W and 87.9 of efficiency while PO reaches 241 W and 84.26 of efficiency, and InC reaches 245 W and 78.1 of efficiency. The ANN also tracks the MPP faster and with steadier behaviour when irradiance varies. These outcomes confirm that ANN MPPT can raise the energy output of PV systems.

 The study presents an overview and a simulation of maximum power point tracking MPPT for Photovoltaic PV systems that uses an artificial neural network ANN controller as proof of concept. Solar energy must be harvested with high efficien-cy as the world turns to renewables. The usual Perturb and Observe PO and Incremental InC method loses power by oscil-lating around the Maximum Power Point MPP and reacts slowly to sudden weather changes. The work therefore tests an ANN as a better choice. The authors survey earlier ANN MPPT studies that cover many network types, training schemes and mixed strategies. They then build a MATLABSimulink model that runs an ANN controller and a PO controller on the same PV array. The ANN learns from Istanbul 2020 weather data. The results show the ANN reaches 252 W and 87.9 of efficiency while PO reaches 241 W and 84.26 of efficiency, and InC reaches 245 W and 78.1 of efficiency. The ANN also tracks the MPP faster and with steadier behaviour when irradiance varies. These outcomes confirm that ANN MPPT can raise the energy output of PV systems.

 The study presents an overview and a simulation of maximum power point tracking MPPT for Photovoltaic PV systems that uses an artificial neural network ANN controller as proof of concept. Solar energy must be harvested with high efficien-cy as the world turns to renewables. The usual Perturb and Observe PO and Incremental InC method loses power by oscil-lating around the Maximum Power Point MPP and reacts slowly to sudden weather changes. The work therefore tests an ANN as a better choice. The authors survey earlier ANN MPPT studies that cover many network types, training schemes and mixed strategies. They then build a MATLABSimulink model that runs an ANN controller and a PO controller on the same PV array. The ANN learns from Istanbul 2020 weather data. The results show the ANN reaches 252 W and 87.9 of efficiency while PO reaches 241 W and 84.26 of efficiency, and InC reaches 245 W and 78.1 of efficiency. The ANN also tracks the MPP faster and with steadier behaviour when irradiance varies. These outcomes confirm that ANN MPPT can raise the energy output of PV systems.

 Bu çalışma, kavram kanıtı olarak YSA denetleyicisi kullanan PV sistemleri için MPPTye genel bir bakış ve bir simülasyon sunmaktadır. Dünya yenilenebilir enerji kaynaklarına yönelirken güneş enerjisi yüksek verimlilikle toplanmalıdır. Alışılagelmiş PO ve InC yöntemi, MPP etrafında salınarak güç kaybeder ve ani hava değişikliklerine yavaş tepki verir. Bu nedenle çalışma, YSAyı daha iyi bir seçenek olarak test etmektedir. Yazarlar, birçok ağ türünü, eğitim şemasını ve karma stratejileri kapsayan daha önceki YSA tabanlı MPPT çalışmalarını incelemektedir. Daha sonra aynı PV dizisi üzerinde bir YSA kontrolörü ve bir PO kontrolörü çalıştıran bir MATLAB Simulink modeli oluşturmuşturlardır. YSA, İstanbul 2020 hava durumu verileriyle eğitilmiştir. Sonuçlar, YSAnın 252 W ve yüzde 87.9 verimliliğe ulaştığını, POnun 241 W ve yüzde 84.26 verimliliğe ulaştığını ve InCnin 245 W ve yüzde 78.1 verimliliğe ulaştığını göstermiştir. YSA ayrıca MPPyi daha hızlı ve ışınım değiştiğinde daha istikrarlı bir davranışla takip etmektedir. Bu sonuçlar, YSA tabanlı MPPTnin PV sistemlerinin enerji çıkışını artırabileceğini doğrulamaktadır.

Article Details

An Overview of ANN based MPPT and an Example

Authors:

Mehdi Rezaee | Yusuf Gürcan Şahin

Publisher:

İstanbul Gelisim University

Main Topics:

Fotovoltaik Güç Sistemleri

Journal:

International Journal of Engineering Technologies Vol.10 Issue 1, pp.9-22

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001347930

pISSN:

2149-0104

Artificial Neural Network (ANN)

Maximum Power Point Tracking (MPPT)

Photovoltaics

Renewable energyYapay Sinir Ağları (YSA)

En Yüksek Güç Noktası Takibi (EYGNT)(MPPT)

Fotovoltaik

Yenilenebilir enerji

Issues

Article Details

Authors: Mehdi Rezaee | Yusuf Gürcan Şahin

Publisher: İstanbul Gelisim University

Main Topics: Fotovoltaik Güç Sistemleri

Journal: International Journal of Engineering Technologies Vol.10 Issue 1, pp.9-22

Document Type: Article

Publication Date: 7/2025

Document Language: English

Document ID (Org No.): 2001347930

pISSN: 2149-0104

Issues

Authors:

Mehdi Rezaee | Yusuf Gürcan Şahin

Publisher:

İstanbul Gelisim University

Main Topics:

Fotovoltaik Güç Sistemleri

Journal:

International Journal of Engineering Technologies Vol.10 Issue 1, pp.9-22

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001347930

pISSN:

2149-0104