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MENEZES, T. S.. Islanding Detection for Distributed Generators using S-transform and Artificial Neural Networks with Extreme Learning Machine. 67 p. Master Dissertation – São Carlos School of Engineering, University of São Paulo, São Carlos, 2019. The connection of distributed generators (DG) in the distribution system has been intensified in the recent years. In this scenario, the increase of DG can bring some benefits, such as generation redundancy and reduction of power losses. On the other hand, the problem of islanding is also been highlighted. Currently, there are already consolidated techniques for islanding detection, and passive techniques are among the most used ones. However, the passive techniques are very dependent of the power unbalance between the generation and the loads at the moment of the islanding in order to actuate properly. If the power mismatch is small, the passive techniques tend to not identify the islanding, generating the so called Non-Detection Zones (NDZ). To mitigate this issue, the research of intelligent passive techniques based in machine learning is becoming more common. In this study, an anti-islanding protection based on Artificial Neural Networks (ANN) was modelled. The islanding classification is done based on the frequency spectrum of the DG’s terminal voltages using the Stockwell Transform, or just S-Transform (ST). Another important point of the methodology was the implementation of an event detection stage, also based on the energies of the voltages frequency spectrum, to avoid the constant execution of the classifier. Therefore, the ANN will only classify the event after receiving a trigger signal from the event detection stage. To train the ANN, two different algorithms were tested: the classic Backpropagation and the Extreme Learning Machine (ELM). It is noteworthy the better performance obtained with the neural networks trained by the ELM, which had a greater capacity of generalization, hence resulting in higher success rates. In general, after being compared with conventional passive techniques for islanding detection, the proposed protection was more accurate and with a much smaller detection time, being less than 2 cycles. Finally, the analysis of the NDZ for the proposed protection and the conventional techniques was carried out, as it is a very important feature for anti-islanding protection, but is not commonly addressed for intelligent passive techniques. In this analysis, the islanding detection method proposed again overcame the conventional techniques, presenting a much smaller NDZ.

Detecção de ilhamento de Geradores Distribuídos utilizando Transformada S e Redes Neurais Artificiais com Máquina de Aprendizado Extremo