Abstract
The anti-islanding protection of distributed generators is typically performed by conventional schemes that monitor the magnitude and the frequency of voltage signals. However, one of the main issues in setting up such schemes is to identify and differentiate magnitude and frequency variations caused by an islanding event from other disturbances that may occur in the system, such as a voltage sag or swell. In this context, an algorithm based on Artificial Neural Network (ANN) can be used to identify voltage waveform patterns produced by the distributed generator, making it possible to obtain accurate responses about islanding events. Nevertheless, the ANN training process is not simple, since it involves the definition of the ANN architecture, the data window length, the sampling rate and the selection of a representative training set for the analyzed power grid. Along these lines, this paper aims to discuss the fundamental aspects for training an ANN in islanding detection of photovoltaic distributed generators.
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Buscariolli, L., dos Santos, R.C., Pavani, A.P.G. (2023). Methodology for Training Artificial Neural Networks for Islanding Detection of Photovoltaic Distributed Generators. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_30
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