Alper Nabi AKPOLAT, Erkan DURSUN, Ahmet Emin KUZUCUOĞLU

Rüzgâr Enerjisi Dönüşüm Sistemlerinde bir DC-DC Güç Dönüştürücüsünün Denetimli Öğrenme Destekli Kontrolü

Son on yıllarda, elektrik enerjisi sistemlerinde yenilenebilir enerji kaynaklarının yüksek oranda nüfuzunu yaygınlaştırmak için dağıtılmış enerji kaynakları öne çıkmıştır. Küçük rüzgâr türbinlerinin kolay erişilebilirlik durumu nedeniyle, rüzgâr enerjisi dönüşüm sistemleri özellikle rüzgârlı alanlarda küçük müşteriler için elverişli uygulamalardır. Önümüzdeki on yıl muhtemelen dağıtık enerji kaynakları önemli bir artışa tanık olacaktır. Bu bağlamda, rüzgâr enerji dönüşüm sistemleri yaygın olarak tercih edilmektedir, bu nedenle rüzgâr enerjisinin elektrik enerjisine etkin bir şekilde dönüştürülmesi önemli bir konudur. Rüzgâr türbinleri çeşitli topolojilerle şebekeye bağlı veya otonom modda dâhil edilebilirler. Bu makalede, denetimli öğrenme yöntemine dayalı yapay zekâ destekli PI denetleyicisi yardımıyla bir rüzgâr enerji dönüşüm sistemindeki yükselten DC-DC güç dönüştürücüsünün kontrolünü incelemekteyiz. Önerilen yöntemle ilgili olarak, yapay zekânın bir alt kümesi olarak yapay sinir ağları kullanılmaktadır. Önerilen kontrol yönteminin uygulanabilirliğini test etmek ve doğrulamak için, MATLAB/Simulink ortamında bir DC baraya sabit mıknatıslı senkron generatör ile küçük bir rüzgar enerji dönüşüm sistem uygulanmıştır. Sonuçlar, çalışma kapsamında dinamik yanıtın ve daha az karmaşıklığın yüksek doğruluk ile elde edildiğini göstermektedir.

Anahtar Kelimeler:

Rüzgâr Enerjisi Dönüşüm Sistemleri, Yapay Zeka, Yapay Sinir Ağı, DC-DC Güç Dönüştürücüler

Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems

Over the last decades, to adopt high penetration of renewable energy sources (RESs) in electrical energy systems, distributed energy resources (DERs) have become prominent. Due to easy attainability status of small wind turbines (WTs), wind energy conversion systems (WECSs) are feasible applications for small customers, especially in windy areas. The next decade is likely to witness a considerable rise in DERs. In this context, WECSs are preferred broadly, thus harvesting wind energy into electrical energy effectively is a substantial issue. WTs can be got involved in the grid-connected or autonomous mode with a variety of topologies. In this paper, we examine to control of DC-DC boost converter of a WECS with the help of artificial intelligence (AI)-aided PI controller based on supervised learning method. Regarding the proposed method, artificial neural networks (ANNs) as a subset of AI are utilized. To test and ensure the applicability of the proposed control method, a small WECS with a permanent magnet synchronous generator (PMSG) connected a DC bus was implemented in MATLAB/Simulink environment. The proposed ANN scheme has reached a high accuracy rate with an overall mean squared error (MSE) equal to 7.4e-08. The results present that dynamic response and less complexity with a high accuracy rate have been obtained under study. The main target of this study is to reduce the number of sensors in the control layer. Thus, a cost-effective and more reliable structure is obtained with fewer sensor requirements.

Keywords:

Wind Energy Conversion Systems, Artificial Intelligence, Artificial Neural Networks, DC-DC Power Converters,

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