Controlling of artificial neural network for fault diagnosis of photovoltaic array
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
High penetration of photovoltaic (PV) systems is expected to play important roles as power generation source in the near future. One of the typical deployments of PV systems is without supervisory mechanisms to monitor the physical conditions of cells or modules. In the longer term operation, the cells or modules may undergo fault conditions since they are exposure to the environment. Manually module checking is not recommended in this case because of time-consuming, less accuracy and potentially danger to the operator. Therefore, provision of early automatic diagnosis technique with quick and efficient responses is highly necessary. Since high accuracy is the important issue in the diagnosis problems, the paper present fault diagnosis method using three-layered artificial neural network. A single artificial neural network (ANN) is not suitable to provide precise solution for this fault identification. Therefore, several ANNs are developed, then automatic control based module voltage terminal is established. The proposed method is simple and accurate to detect the exact location of short-circuit condition of PV modules in array. © 2011 IEEE.
Açıklama
IEEE;Power and Energy Society (IEEE PES)
2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011 -- 25 September 2011 through 28 September 2011 -- Hersonisos, Crete -- 87693
2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011 -- 25 September 2011 through 28 September 2011 -- Hersonisos, Crete -- 87693
Anahtar Kelimeler
fault diagnosis, fault location, PV array, short-circuit condition, TFFN
Kaynak
2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011
WoS Q Değeri
Scopus Q Değeri
N/A
