Thông tin tài liệu
Nhan đề : |
Development of a machine-learning-based method for early fault detection in photovoltaic systems |
Tác giả : |
Stylianos, Voutsinas Dimitrios, Karolidis Ioannis, Voyiatzis |
Năm xuất bản : |
2023 |
Nhà xuất bản : |
Springer |
Tóm tắt : |
In 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. |
Mô tả: |
CC BY |
URI: |
https://link.springer.com/article/10.1186/s44147-023-00200-0 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8068 |
Bộ sưu tập |
OER - Kỹ thuật điện; Điện tử - Viễn thông |
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