Photovoltaic System Fault Detection Based On Ensemble Learning

Year 2019, , 83 - 91, 30.04.2019

Ceyhun Kapucu , Mete Çubukçu

https://doi.org/10.17671/gazibtd.508475

Abstract

An accurate fault detection capability for photovoltaic (PV) systems can improve PV system productivity by reducing operational costs and possible downtimes caused by a failure. In this paper, a fault detection method for PV systems is proposed. The proposed method is based on the use of an ensemble learning based model for classifying faults in PV systems. Ensemble learning combines the predictions of different algorithms in order to improve generalizability and robustness over a single learning algorithm. In this study, an ensemble learning model is built from some learning algorithms that commonly used in the classification problems. The ensemble model is then improved via parameter optimization. Each learning algorithms and the ensemble model that combines them are compared in terms of their prediction accuracy. The proposed method was implemented using Python with Scikit-learn machine learning library. The experimental validation of the method has been performed using electrical and meteorological measurements data from a residential PV system installed in Muğla (Turkey). Results show that, with an optimized ensemble learning model, the proposed method not only improves the classification accuracy but also has a strong generalization ability for PV system fault diagnosis.

Keywords

fault detection, classification, ensemble learning

Fotovoltaik Sistemlerde Topluluk Öğrenmesi Temelli Hata Tespiti

Abstract

Fotovoltaik (FV) sistemler için doğru bir hata tespit yeteneği, işletme maliyetlerini ve bir arıza nedeniyle oluşabilecek devre dışı kalma sürelerini azaltarak FV sistemin verimliliğini artırabilir. Bu çalışmada, FV sistemler için bir hata tespit yöntemi önerilmiştir. Önerilen yöntem, topluluk öğrenmesi temelli bir modelin FV sistemlerdeki hataları sınıflandırmak amacıyla kullanılmasına dayanmaktadır. Topluluk öğrenmesi yöntemi, tek bir öğrenme algoritmasının genelleme yeteneğinin ve sağlamlığının üstüne çıkabilmek için farklı algoritmaların tahminlerini birleştirir. Bu çalışmada, sınıflandırma problemlerinde yaygın olarak kullanılan bazı öğrenme algoritmalarından bir topluluk öğrenmesi modeli oluşturulmuştur. Topluluk modeli, daha sonra parametre optimizasyonu uygulanarak geliştirilmiştir. Öğrenme algoritmalarının her biri ve bunları birleştiren topluluk modeli tahmin doğrulukları açısından karşılaştırılmıştır. Önerilen yöntem, Scikit-learn makine öğrenme kütüphanesi ile Python kullanılarak gerçekleştirilmiştir. Yöntemin deneysel geçerliliği Muğla'da (Türkiye) kurulu bir konut tipi FV sistemden elektriksel ve meteorolojik ölçüm verileri kullanılarak yapılmıştır. Sonuçlar, optimize edilmiş bir topluluk öğrenmesi modeliyle, önerilen yöntemin yalnızca sınıflandırma doğruluğunu geliştirmediğini, aynı zamanda fotovoltaik sistem hata tespiti için güçlü bir genelleme yeteneğine de sahip olduğunu göstermektedir.

Keywords

hata tespiti, sınıflandırma, topluluk öğrenmesi

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