Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems

Abstract In this paper, we consider a machine learning approach merged with statistical testing hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The developed method makes use of a machine learning based Gaussian process regression (GPR) technique as a modeling framework, while a generalized likelihood ratio test (GLRT) chart is applied to detect PV system faults. The developed GPR-based GLRT approach is assessed using simulated and real PV data through monitoring the key PV system variables (current, voltage, and power). The computation time, missed detection rate (MDR), and false alarm rate (FAR) are computed to evaluate the fault detection performance of the proposed approach.

[1]  Ye Zhao,et al.  Line–Line Fault Analysis and Protection Challenges in Solar Photovoltaic Arrays , 2013, IEEE Transactions on Industrial Electronics.

[2]  Hazem Nounou,et al.  Wavelet optimized EWMA for fault detection and application to photovoltaic systems , 2018, Solar Energy.

[3]  R. Mahalakshmi,et al.  Fault Identification and Islanding in DC Grid Connected PV System , 2016 .

[4]  Giorgio Sulligoi,et al.  A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks , 2016 .

[5]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  D. Steinberg.  CART: Classification and Regression Trees , 2009 .

[7]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[8]  Hazem Nounou,et al.  Kernel PLS-based GLRT method for fault detection of chemical processes , 2016 .

[9]  Zhehan Yi,et al.  Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine , 2017, IEEE Transactions on Industrial Electronics.

[10]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[11]  C. Birk Jones,et al.  Automatic fault classification of photovoltaic strings based on an in situ IV characterization system and a Gaussian process algorithm , 2016, 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC).

[12]  Narciso Moreno-Alfonso,et al.  Intelligent waste-water treatment with neural-networks , 2001 .

[13]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[14]  Zhehan Yi,et al.  Fault Detection for Photovoltaic Systems Based on Multi-Resolution Signal Decomposition and Fuzzy Inference Systems , 2017, IEEE Transactions on Smart Grid.

[15]  B. Lehman,et al.  Decision tree-based fault detection and classification in solar photovoltaic arrays , 2012, 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

[16]  Hazem Nounou,et al.  Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems , 2018, International Transactions on Electrical Energy Systems.

[17]  R. Teodorescu,et al.  Detection of increased series losses in PV arrays using Fuzzy Inference Systems , 2012, 2012 38th IEEE Photovoltaic Specialists Conference.

[18]  Hazem Nounou,et al.  Statistical fault detection using PCA-based GLR hypothesis testing , 2013 .

[19]  Alexander J. Smola,et al.  Sparse Greedy Gaussian Process Regression , 2000, NIPS.

[20]  Meng-Hui Wang,et al.  A Novel Fault Diagnosis Method Based-on Modified Neural Networks for Photovoltaic Systems , 2010, ICSI.

[21]  C. Birk Jones,et al.  Photovoltaic system fault detection and diagnostics using Laterally Primed Adaptive Resonance Theory neural network , 2015, 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC).

[22]  A. Kai Qin,et al.  Evolutionary extreme learning machine , 2005, Pattern Recognit..

[23]  Mahmoud Nasr,et al.  Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT , 2012 .

[24]  Abdelhamid Rabhi,et al.  Comprehensive Modulation and Classification of Faults and Analysis Their Effect in DC Side of Photovoltaic System , 2013 .

[25]  Yassine Djebbar,et al.  Neural Network Prediction of Air Stripping KLa , 2002 .

[26]  Hazem Nounou,et al.  An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test , 2018, Energy.

[27]  Jan Peters,et al.  Model Learning with Local Gaussian Process Regression , 2009, Adv. Robotics.

[28]  J. Ogutu,et al.  Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions , 2012, BMC Proceedings.

[29]  Violeta Holmes,et al.  Simultaneous fault detection algorithm for grid-connected photovoltaic plants , 2017 .

[30]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[31]  Aman Ullah,et al.  On the Robustness of LM, LR, and W Tests in Regression Models , 1984 .

[32]  D. Farina,et al.  Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  Il-Song Kim.  On-line fault detection algorithm of a photovoltaic system using wavelet transform , 2016 .

[34]  Maryam A. Hejazi,et al.  The Comprehensive Study of Electrical Faults in PV Arrays , 2016, J. Electr. Comput. Eng..

[35]  Iman Sadeghkhani,et al.  Fault detection for PV systems using Teager–Kaiser energy operator , 2018 .

[36]  Hazem N. Nounou,et al.  Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems , 2017, IEEE Transactions on NanoBioscience.

[37]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[38]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[39]  Hazem Nounou,et al.  Multiscale PLS-based GLRT for fault detection of chemical processes , 2017 .

[40]  Luis Castañer,et al.  Solar Cells: Materials, Manufacture and Operation , 2004 .

[41]  Violeta Holmes,et al.  Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system , 2017 .

[42]  Hazem Nounou,et al.  Fault detection using multiscale PCA-based moving window GLRT , 2017 .

[43]  H. Mekki,et al.  Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules , 2016, Simul. Model. Pract. Theory.

[44]  Hazem Nounou,et al.  Kernel PCA-based GLRT for nonlinear fault detection of chemical processes , 2016 .

[45]  Maged M. Hamed,et al.  Prediction of wastewater treatment plant performance using artificial neural networks , 2004, Environ. Model. Softw..

[46]  Christopher M. Bishop,et al.  Variational Relevance Vector Machines , 2000, UAI.

[47]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..