Yapay sinir ağına dayalı bir FV güç santralinin arızaların iyileştirilmesi yoluyla analizi

Year 2023, , 360 - 366, 15.04.2023

Altan Gencer

https://doi.org/10.28948/ngumuh.1252244

Cited By: 1

Abstract

Hem yenilenebilir enerji kaynaklardan bir şebeke gerilimi elde etme hem de yüksek elektrik faturalarını azaltma ihtiyacından kaynaklanan endüstriyel alanlarda yenilenebilir enerji kaynaklarının giderek daha fazla benimsenmesi bu sistemlerin güç kalitesini iyileştirebilecek çözümlere olan talebin artmasına neden olmaktadır. Fotovoltaik enerji santralinin (FVES) güç kalitesinin arttırılması için dc-kıyıcı devre koruma sistemine dayalı bir fotovoltaik enerji santrali önerilmiştir. DC-kıyıcı devre koruma sisteminin temel amacı, FV panelleri şebeke arızaları sırasında oluşan yüksek dc voltajın zararlı etkilerinden korumaktır. Uyarlanabilir sinir ağı kontrol sistemi, hem T tipi eviricinin denetim sistemine hem de dc-kıyıcı devre koruma sistemine uygulanmıştır. Önerilen koruma sistemi, şebeke arızası sırasında benzetim sonuçlarıyla geçerliliğini doğrulamak için 250 kW' lık bir FVES ile uygulanmıştır. Bu yaklaşım potansiyel olarak FVES’ in güvenilirliğini ve istikrarını artırabilir ve FVES’ in şebekeye bağlantısında katkıda bulunabilir.

Keywords

Fotovoltaik enerji santrali, yapay sinir ağı, dc-kıyıcı devresi

Analysis of fault ride through the improvement of PV power plant based on artificial neural network

Abstract

The increasing adoption of renewable energy sources in industrial areas, driven by the need to both obtain a grid voltage from renewable energy sources and reduce high electricity bills, has led to an increased demand for solutions that can improve the power quality of these systems. A photovoltaic power plant (PVPP) based on a dc-chopper circuit protection system is proposed for the enhancement of the power quality of PVPP. The aim of the presented protection method is to protect the panels in the PVPP from the harmful effects of the high dc voltage that occurs during grid faults. The adaptive neural network control system is applied in both the T-type inverter and the dc-chopper circuit protection system. The proposed protection system is implemented with a 250 kW PVPP to verify its validity with simulation results during grid fault. This approach can potentially improve the reliability and stability of PVPP, and contribute to the maintain of PVPP into the grid.

Keywords

Photovoltaic power plant, artificial neural network, Dc-chopper circuit

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