Study of joint temporal-spatial distribution of array output for large-scale photovoltaic plant and its fault diagnosis application

Abstract The temporal-spatial distribution of photovoltaic (PV) array output in large-scale PV plant has following features: (i) In the DC (direct current) side of PV plant, the outputs from different arrays display a substantial correlation with each other. (ii) The dynamic difference for a PV array is covered by the random fluctuation of PV output. (iii) It is difficult to explain the occurrence and evolution process of PV faults with operation data only. Currently, most fault diagnosis methods for the PV arrays do not take advantage of the temporal-spatial distribution information contained in the operation data. To solve these above problem, a new fault diagnosis method using the spatio-temporal distribution characteristics of photovoltaic array output is proposed. Here, the temporal fluctuation and spatial distribution characteristics of PV array output under different fault conditions were analyzed. The spatio-temporal composite function is constructed, and then used to set fuzzy rules for fault diagnosis of the PV array. Finally, an example is used to verify the effectiveness of the proposed method, and the results show that the method can describe the characters of spatial and temporal distribution of PV output under faults conditions, and it can effectively classify different faults.

[1]  Adrián Rodríguez Ramos,et al.  An approach to fault diagnosis with online detection of novel faults using fuzzy clustering tools , 2018, Expert Syst. Appl..

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

[3]  Nan Guan,et al.  Photovoltaic Array Fault Detection by Automatic Reconfiguration , 2017 .

[4]  Siva Ramakrishna Madeti,et al.  Modeling of PV system based on experimental data for fault detection using kNN method , 2018, Solar Energy.

[5]  Siva Ramakrishna Madeti,et al.  A comprehensive study on different types of faults and detection techniques for solar photovoltaic system , 2017 .

[6]  Zhenghai Liao,et al.  A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization-Back-Propagation Neural Network , 2017 .

[7]  Li Shang,et al.  Hierarchical context-aware anomaly diagnosis in large-scale PV systems using SCADA data , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

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

[9]  Xin Liu,et al.  A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm , 2018 .

[10]  Ying Sun,et al.  Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches , 2018 .

[11]  Soteris A. Kalogirou,et al.  Fault detection and diagnosis methods for photovoltaic systems: A review , 2018, Renewable and Sustainable Energy Reviews.

[12]  Violeta Holmes,et al.  Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection , 2018 .

[13]  Elyes Garoudja,et al.  An enhanced machine learning based approach for failures detection and diagnosis of PV systems , 2017 .

[14]  S. Iniyan,et al.  Applications of fuzzy logic in renewable energy systems – A review , 2015 .

[15]  Fengjie Sun,et al.  Fault Diagnosis Model of Photovoltaic Array Based on Least Squares Support Vector Machine in Bayesian Framework , 2017 .

[16]  Rabah Benkercha,et al.  Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system , 2018, Solar Energy.

[17]  N. Rajasekar,et al.  A comprehensive review on protection challenges and fault diagnosis in PV systems , 2018, Renewable and Sustainable Energy Reviews.

[18]  Zhong Jinyua.  A Fault Diagnosing Method in Power Transmission Systems Based on Time Series Similarity Matching , 2015 .

[19]  B. Lehman,et al.  Outlier detection rules for fault detection in solar photovoltaic arrays , 2013, 2013 Twenty-Eighth Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

[20]  Yang Hu,et al.  Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model , 2018, Solar Energy.

[21]  D. Cormode,et al.  PV system power loss and module damage due to partial shade and bypass diode failure depend on cell behavior in reverse bias , 2015, 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC).

[22]  Ying Sun,et al.  Statistical fault detection in photovoltaic systems , 2017 .

[23]  Abdelhamid Rabhi,et al.  Real Time Fault Detection in Photovoltaic Systems , 2017 .

[24]  P. Guerriero,et al.  Automatic edge identification for accurate analysis of thermographic images of solar panels , 2017, 2017 6th International Conference on Clean Electrical Power (ICCEP).

[25]  Ying Chen,et al.  Development of fast simulation models for photovoltaic generation system based on Simulink , 2015, 2015 IEEE Energy Conversion Congress and Exposition (ECCE).

[26]  Toyonari Shimakage,et al.  Development of fault detection system in PV system , 2011, 2011 IEEE 33rd International Telecommunications Energy Conference (INTELEC).

[27]  Siva Ramakrishna Madeti,et al.  Online modular level fault detection algorithm for grid-tied and off-grid PV systems , 2017 .

[28]  Lijun Wu,et al.  Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics , 2017 .

[29]  Fu Guoxua.  Analytical Method and Application of Current Discrete Rate of PV Power Station Based on Junction Box String , 2014 .

[30]  Zhehan Yi,et al.  A novel detection algorithm for Line-to-Line faults in Photovoltaic (PV) arrays based on support vector machine (SVM) , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).