Case of study: Photovoltaic faults recognition method based on data mining techniques

被引:13
作者
Serrano-Lujan, Lucia [1 ,2 ]
Cadenas, Jose Manuel [3 ]
Faxas-Guzman, Juan [4 ]
Urbina, Antonio [1 ]
机构
[1] Imperial Coll London, Dept Mat, Exhibit Rd, London SW7 2AZ, England
[2] Tech Univ Cartagena, Dept Elect, Plaza Hosp 1, Cartagena 30202, Spain
[3] Univ Murcia, Dept Informat Engn & Commun, Murcia, Spain
[4] Pontificia Univ Catolica Madre & Maestra, Fac Ciencias Ingn, Campus Santo Tomas Aquino, Santo Domingo, Dominican Rep
关键词
DIAGNOSIS; CLASSIFICATION; AREA;
D O I
10.1063/1.4960410
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Data Mining techniques have been applied to data collected from a 222 kWp CdTe (Cadmium Telluride) photovoltaic (PV) generator to predict faults or special conditions that occurs due to shadows, bad weather, soiling, and technical faults. Five types of errors have been distinguished and its impact on the PV system performance has been evaluated. Up to date, this computing approach has needed the simultaneous measurement of environmental attributes that an array of sensors collected. This study presents a model to assess the state of the PV (photovoltaic) generator and an algorithm that classifies its state without measuring ambient conditions. The result of a 222 kWp CdTe PV case study shows how the application of computing learning algorithms can be used to improve the management and performance of the photovoltaic generators and underlines the environmental parameters as clue attributes to find faults during the PV performance. Although the application of this method requires computational effort, the result deals with an easy-implementing decision tree, which can be installed in small device. Published by AIP Publishing.
引用
收藏
页数:18
相关论文
共 55 条
[1]   Monitoring and smart management for hybrid plants (photovoltaic-generator) in Ghardaia [J].
Adouane, M. ;
Haddadi, M. ;
Touafek, K. ;
AitCheikh, S. .
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2014, 6 (02)
[2]  
AMOOEE G, 2011, INT J COMPUTER SCI I, V8, P425
[3]  
[Anonymous], 2015, TRENDS 2015 PHOT APP
[4]  
[Anonymous], 2014, Technology roadmap: solar photovoltaic energy - 2014 edition
[5]  
[Anonymous], UNDERGRADUATE TOPICS
[6]   CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].
BENJAMINI, Y ;
HOCHBERG, Y .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300
[7]   Neuro-Fuzzy fault detection method for photovoltaic systems [J].
Bonsignore, Luca ;
Davarifar, Mehrdad ;
Rabhi, Abdelhamid ;
Tina, Giuseppe M. ;
Elhajjaji, Ahmed .
6TH INTERNATIONAL CONFERENCE ON SUSTAINABILITY IN ENERGY AND BUILDINGS, 2014, 62 :431-441
[8]   The use of the area under the roc curve in the evaluation of machine learning algorithms [J].
Bradley, AP .
PATTERN RECOGNITION, 1997, 30 (07) :1145-1159
[9]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[10]   Feature subset selection Filter-Wrapper based on low quality data [J].
Cadenas, Jose M. ;
Carmen Garrido, M. ;
Martinez, Raquel .
EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (16) :6241-6252