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Photovoltaic System Fault Detection Based On Ensemble Learning

Year 2019, Volume: 12 Issue: 2, 83 - 91, 30.04.2019
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.

References

  • [1] A. Woyte, M. Richter, D. Moser, S. Mau, N. H. Reich, U. Jahn, “Monitoring of Photovoltaic Systems: Good Practices and Systematic Analyes”, 28th European PV Solar Energy Conference and Exhibition, Paris, France, 2013.
  • [2] P. Guerriero, V. D’Alessandro, L. Petrazzuoli, G. Vallone, and S. Daliento, “Effective real-time performance monitoring and diagnostics of individual panels in PV plants”, 4th International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2013, Hamburg, Germany, 14–19, 2013.
  • [3] B. Ando, S. Baglio, A. Pistorio, G. M. Tina, and C. Ventura, “Sentinella: Smart Monitoring of Photovoltaic Systems at Panel Level”, IEEE Transactions on Instrumentation and Measurement, 64(8), 2188–2199, 2015.
  • [4] W. Chine, a. Mellit, V. Lughi, a. Malek, G. Sulligoi, and a. Massi Pavan, “A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks”, Renewable Energy, 90, 501–512, 2016.
  • [5] H. Mekki, A. Mellit, and H. Salhi, “Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules”, Simulation Modelling Practice and Theory, 67, 1–13, 2016.
  • [6] Y. Zhao, R. Ball, J. Mosesian, J.-F. de Palma, and B. Lehman, “Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays”, IEEE Transactions on Power Electronics, 30(5), 2848–2858, 2015.
  • [7] L. Bonsignore, M. Davarifar, A. Rabhi, G. M. Tina, and A. Elhajjaji, “Neuro-Fuzzy Fault Detection Method for Photovoltaic Systems”, Energy Procedia, 62, 431–441, 2014.
  • [8] D. Riley and J. Johnson, “Photovoltaic prognostics and heath management using learning algorithms”, Conference Record of the IEEE Photovoltaic Specialists Conference, Austin, TX, USA, 1535–1539, 2012.
  • [9] Y. Zhao, L. Yang, B. Lehman, J.-F. de Palma, J. Mosesian, and R. Lyons, “Decision tree-based fault detection and classification in solar photovoltaic arrays”, 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Orlando, FL, USA, 93–99, 2012.
  • [10] S. Spataru, D. Sera, T. Kerekes, and R. Teodorescu, “Diagnostic method for photovoltaic systems based on light I-V measurements”, Solar Energy, 119, 29–44, 2015.
  • [11] P. Ducange, M. Fazzolari, B. Lazzerini, and F. Marcelloni, “An intelligent system for detecting faults in photovoltaic fields”, International Conference on Intelligent Systems Design and Applications, ISDA, Cordoba, Spain, 1341–1346, 2011.
  • [12] L. L. Jiang and D. L. Maskell, “Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods”, 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 1–8, 2015.
  • [13] C. B. Jones, J. S. Stein, S. Gonzalez, and B. H. King, “Photovoltaic system fault detection and diagnostics using Laterally Primed Adaptive Resonance Theory neural network”, 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), New Orleans, Louisiana, USA, 1–6, 2015.
  • [14] M. N. Akram and S. Lotfifard, “Modeling and Health Monitoring of DC Side of Photovoltaic Array”, IEEE Transactions on Sustainable Energy, 6(4), 1245–1253, 2015.
  • [15] K. Chao, P. Chen, M. Wang, and C. Chen, “An Intelligent Fault Detection Method of a Photovoltaic Module Array Using Wireless Sensor Networks”, International Journal of Distributed Sensor Networks, 10(5), 12, 2014.
  • [16] E. Karatepe and T. Hiyama, “Controlling of artificial neural network for fault diagnosis of photovoltaic array”, 2011 16th International Conference on Intelligent System Applications to Power Systems, Crete, Greece, 1–6, 2011.
  • [17] K. H. Chao, C. T. Chen, M. H. Wang, C. F. Wu, “A novel fault diagnosis method based-on modified neural networks for photovoltaic systems”, Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg, 531–539, 2010.
  • [18] Y. Yagi et al., “Diagnostic technology and an expert system for photovoltaic systems using the learning method”, Solar Energy Materials and Solar Cells, 75(3–4), 655–663, 2003.
  • [19] Y. Zhao, F. Balboni, T. Arnaud, J. Mosesian, R. Ball, and B. Lehman, “Fault experiments in a commercial-scale PV laboratory and fault detection using local outlier factor”, 2014 IEEE 40th Photovoltaic Specialist Conference, PVSC 2014, Denver, CO, USA, 3398–3403, 2014.
  • [20] C.-T. Hsieh, H.-T. Yau, and J. Shiu, “Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems”, International Journal of Photoenergy, 2014, 1–9, 2014.
  • [21] K. H. Chao, S. H. Ho, and M. H. Wang, “Modeling and fault diagnosis of a photovoltaic system”, Electric Power Systems Research, 78(1), 97–105, 2008.
  • [22] Z. Y. Wang, C. Lu, and B. Zhou, “Fault diagnosis for rotary machinery with selective ensemble neural networks”, Mechanical Systems and Signal Processing, 113, 112–130, 2018.
  • [23] M. W. Ahmad, M. Mourshed, and Y. Rezgui, “Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression”, Energy, 164, 465–474, 2018.
  • [24] M. Pierro et al., “Multi-Model Ensemble for day ahead prediction of photovoltaic power generation”, Solar Energy, 134, 132–146, 2016.
  • [25] M. Rana, I. Koprinska, and V. G. Agelidis, “Forecasting solar power generated by grid connected PV systems using ensembles of neural networks”, Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland, 1–8, 2015.
  • [26] M. Peker, O. Özkaraca, and B. Kesimal, “Enerji Tasarruflu Bina Tasarımı İçin Isıtma ve Soğutma Yüklerini Regresyon Tabanlı Makine Öğrenmesi Algoritmaları İle Modelleme”, Bilişim Teknolojileri Dergisi, 443–449, 2017.
  • [27] Scikit-learn: Machine Learning in Python, http://scikit-learn.org, 11-07-2017.
  • [28] Python data science, R data science, Python machine learning, R machine learning - Google Trends, https://trends.google.com/trends/explore?date=2016-01-01 2019-02-01&q=Python data science,R data science,Python machine learning,R machine learning, 01-02-2019.
  • [29] T. G. Dietterich, “Ensemble Methods in Machine Learning”, in Oncogene, 12(2), 2000, 1–15.
  • [30] Z. Wang, Y. Wang, and R. S. Srinivasan, “A novel ensemble learning approach to support building energy use prediction”, Energy and Buildings, 159, 109–122, 2018.
  • [31] Z. Chen et al., “Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents”, Energy Conversion and Management, 178(August), 250–264, 2018.
  • [32] D. L. King, W. E. Boyson, and J. A. Kratochvil, Photovoltaic array performance model, Sandia Report No. 2004-3535, 1–19, 2004.

Fotovoltaik Sistemlerde Topluluk Öğrenmesi Temelli Hata Tespiti

Year 2019, Volume: 12 Issue: 2, 83 - 91, 30.04.2019
https://doi.org/10.17671/gazibtd.508475

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.

References

  • [1] A. Woyte, M. Richter, D. Moser, S. Mau, N. H. Reich, U. Jahn, “Monitoring of Photovoltaic Systems: Good Practices and Systematic Analyes”, 28th European PV Solar Energy Conference and Exhibition, Paris, France, 2013.
  • [2] P. Guerriero, V. D’Alessandro, L. Petrazzuoli, G. Vallone, and S. Daliento, “Effective real-time performance monitoring and diagnostics of individual panels in PV plants”, 4th International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2013, Hamburg, Germany, 14–19, 2013.
  • [3] B. Ando, S. Baglio, A. Pistorio, G. M. Tina, and C. Ventura, “Sentinella: Smart Monitoring of Photovoltaic Systems at Panel Level”, IEEE Transactions on Instrumentation and Measurement, 64(8), 2188–2199, 2015.
  • [4] W. Chine, a. Mellit, V. Lughi, a. Malek, G. Sulligoi, and a. Massi Pavan, “A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks”, Renewable Energy, 90, 501–512, 2016.
  • [5] H. Mekki, A. Mellit, and H. Salhi, “Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules”, Simulation Modelling Practice and Theory, 67, 1–13, 2016.
  • [6] Y. Zhao, R. Ball, J. Mosesian, J.-F. de Palma, and B. Lehman, “Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays”, IEEE Transactions on Power Electronics, 30(5), 2848–2858, 2015.
  • [7] L. Bonsignore, M. Davarifar, A. Rabhi, G. M. Tina, and A. Elhajjaji, “Neuro-Fuzzy Fault Detection Method for Photovoltaic Systems”, Energy Procedia, 62, 431–441, 2014.
  • [8] D. Riley and J. Johnson, “Photovoltaic prognostics and heath management using learning algorithms”, Conference Record of the IEEE Photovoltaic Specialists Conference, Austin, TX, USA, 1535–1539, 2012.
  • [9] Y. Zhao, L. Yang, B. Lehman, J.-F. de Palma, J. Mosesian, and R. Lyons, “Decision tree-based fault detection and classification in solar photovoltaic arrays”, 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Orlando, FL, USA, 93–99, 2012.
  • [10] S. Spataru, D. Sera, T. Kerekes, and R. Teodorescu, “Diagnostic method for photovoltaic systems based on light I-V measurements”, Solar Energy, 119, 29–44, 2015.
  • [11] P. Ducange, M. Fazzolari, B. Lazzerini, and F. Marcelloni, “An intelligent system for detecting faults in photovoltaic fields”, International Conference on Intelligent Systems Design and Applications, ISDA, Cordoba, Spain, 1341–1346, 2011.
  • [12] L. L. Jiang and D. L. Maskell, “Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods”, 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 1–8, 2015.
  • [13] C. B. Jones, J. S. Stein, S. Gonzalez, and B. H. King, “Photovoltaic system fault detection and diagnostics using Laterally Primed Adaptive Resonance Theory neural network”, 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), New Orleans, Louisiana, USA, 1–6, 2015.
  • [14] M. N. Akram and S. Lotfifard, “Modeling and Health Monitoring of DC Side of Photovoltaic Array”, IEEE Transactions on Sustainable Energy, 6(4), 1245–1253, 2015.
  • [15] K. Chao, P. Chen, M. Wang, and C. Chen, “An Intelligent Fault Detection Method of a Photovoltaic Module Array Using Wireless Sensor Networks”, International Journal of Distributed Sensor Networks, 10(5), 12, 2014.
  • [16] E. Karatepe and T. Hiyama, “Controlling of artificial neural network for fault diagnosis of photovoltaic array”, 2011 16th International Conference on Intelligent System Applications to Power Systems, Crete, Greece, 1–6, 2011.
  • [17] K. H. Chao, C. T. Chen, M. H. Wang, C. F. Wu, “A novel fault diagnosis method based-on modified neural networks for photovoltaic systems”, Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg, 531–539, 2010.
  • [18] Y. Yagi et al., “Diagnostic technology and an expert system for photovoltaic systems using the learning method”, Solar Energy Materials and Solar Cells, 75(3–4), 655–663, 2003.
  • [19] Y. Zhao, F. Balboni, T. Arnaud, J. Mosesian, R. Ball, and B. Lehman, “Fault experiments in a commercial-scale PV laboratory and fault detection using local outlier factor”, 2014 IEEE 40th Photovoltaic Specialist Conference, PVSC 2014, Denver, CO, USA, 3398–3403, 2014.
  • [20] C.-T. Hsieh, H.-T. Yau, and J. Shiu, “Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems”, International Journal of Photoenergy, 2014, 1–9, 2014.
  • [21] K. H. Chao, S. H. Ho, and M. H. Wang, “Modeling and fault diagnosis of a photovoltaic system”, Electric Power Systems Research, 78(1), 97–105, 2008.
  • [22] Z. Y. Wang, C. Lu, and B. Zhou, “Fault diagnosis for rotary machinery with selective ensemble neural networks”, Mechanical Systems and Signal Processing, 113, 112–130, 2018.
  • [23] M. W. Ahmad, M. Mourshed, and Y. Rezgui, “Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression”, Energy, 164, 465–474, 2018.
  • [24] M. Pierro et al., “Multi-Model Ensemble for day ahead prediction of photovoltaic power generation”, Solar Energy, 134, 132–146, 2016.
  • [25] M. Rana, I. Koprinska, and V. G. Agelidis, “Forecasting solar power generated by grid connected PV systems using ensembles of neural networks”, Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland, 1–8, 2015.
  • [26] M. Peker, O. Özkaraca, and B. Kesimal, “Enerji Tasarruflu Bina Tasarımı İçin Isıtma ve Soğutma Yüklerini Regresyon Tabanlı Makine Öğrenmesi Algoritmaları İle Modelleme”, Bilişim Teknolojileri Dergisi, 443–449, 2017.
  • [27] Scikit-learn: Machine Learning in Python, http://scikit-learn.org, 11-07-2017.
  • [28] Python data science, R data science, Python machine learning, R machine learning - Google Trends, https://trends.google.com/trends/explore?date=2016-01-01 2019-02-01&q=Python data science,R data science,Python machine learning,R machine learning, 01-02-2019.
  • [29] T. G. Dietterich, “Ensemble Methods in Machine Learning”, in Oncogene, 12(2), 2000, 1–15.
  • [30] Z. Wang, Y. Wang, and R. S. Srinivasan, “A novel ensemble learning approach to support building energy use prediction”, Energy and Buildings, 159, 109–122, 2018.
  • [31] Z. Chen et al., “Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents”, Energy Conversion and Management, 178(August), 250–264, 2018.
  • [32] D. L. King, W. E. Boyson, and J. A. Kratochvil, Photovoltaic array performance model, Sandia Report No. 2004-3535, 1–19, 2004.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

Ceyhun Kapucu 0000-0003-0563-235X

Mete Çubukçu 0000-0001-5060-4302

Publication Date April 30, 2019
Submission Date January 4, 2019
Published in Issue Year 2019 Volume: 12 Issue: 2

Cite

APA Kapucu, C., & Çubukçu, M. (2019). Fotovoltaik Sistemlerde Topluluk Öğrenmesi Temelli Hata Tespiti. Bilişim Teknolojileri Dergisi, 12(2), 83-91. https://doi.org/10.17671/gazibtd.508475