A new artificial neural network based method for islanding detection of distributed generators
| Autor: | Mário Oleskovicz, Denis Vinicius Coury, Victor Luiz Merlin, Ricardo Caneloi dos Santos, J. C. M. Vieira, Ahda P. Grilo |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
Training set Artificial neural network business.industry 020209 energy 020208 electrical & electronic engineering Energy Engineering and Power Technology 02 engineering and technology computer.software_genre Machine learning Set (abstract data type) Distributed generation 0202 electrical engineering electronic engineering information engineering Islanding Waveform Data mining Artificial intelligence Electrical and Electronic Engineering business REDES NEURAIS computer Data selection Voltage |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | This paper presents an artificial neural network (ANN) based method for islanding detection of distributed synchronous generators. The proposed method takes advantage of ANN as pattern classifiers. It is capable of identifying the islanding condition based on samples of the voltage waveform measured at the distributed generator terminals only, which is an important advantage over other ANN-based anti-islanding methods. Moreover, the proposed method is robust against false operation. In order to create a training data set for the ANN, a data selection procedure has been proposed, so that the ANN could be trained more effectively, which has contributed positively to the good performance of the method. The concept of the time-performance region has been introduced to assess the method performance, as well as the non-detection zones. A detailed discussion about the data sampling rate to feed the proposed method has also been conducted, so that the computational burden can be faced as an important factor to assess its performance. |
| Databáze: | OpenAIRE |
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