Voltage Sag Source Identification Based on Few-Shot Learning
نویسندگان
چکیده
منابع مشابه
Few-shot Learning
Though deep neural networks have shown great success in the large data domain, they generally perform poorly on few-shot learning tasks, where a classifier has to quickly generalize after seeing very few examples from each class. The general belief is that gradient-based optimization in high capacity classifiers requires many iterative steps over many examples to perform well. Here, we propose ...
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Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by leveraging what the syntax and semantics of the surrounding words tells us. ...
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Power quality monitors (PQM) are required to be installed in a power supply network in order to assess power quality (PQ) disturbances such as voltage sags. However, with few PQMs installation, it is difficult to pinpoint the exact location of voltage sag. This paper proposes a new method for identifying the voltage sag source location by using the artificial neural network (ANN). Radial basis ...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2953226