Item Infomation

Full metadata record
DC FieldValueLanguage
dc.contributor.authorStylianos, Voutsinas-
dc.contributor.authorDimitrios, Karolidis-
dc.contributor.authorIoannis, Voyiatzis-
dc.date.accessioned2023-04-19T04:32:39Z-
dc.date.available2023-04-19T04:32:39Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1186/s44147-023-00200-0-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/8068-
dc.descriptionCC BYvi
dc.description.abstractIn the process of the decarbonization of energy production, the use of photovoltaic systems (PVS) is an increasing trend. In order to optimize the power generation, the fault detection and identification in PVS is significant. The purpose of this work is the study and implementation of such an algorithm, for the detection as many as faults arising on the DC side of a photovoltaic system. A machine learning technique was chosen. The dataset used to train the algorithm was based on a year’s worth of irradiance and temperature data, as well as data from the PV cell used. The method uses logistic regression with cross validation as a new approach to detect and identify faults in PVS.vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subjectphotovoltaic systemsvi
dc.subjectmachine-learning-based methodvi
dc.titleDevelopment of a machine-learning-based method for early fault detection in photovoltaic systemsvi
dc.typeBookvi
Appears in Collections
OER - Kỹ thuật điện; Điện tử - Viễn thông

Files in This Item: