Abstract
This research paper presents a method that uses measurements of voltages angles, as provided by phasor measurement units (PMU), to accurately detect the sudden disconnection of a generation unit from a power grid. Results in this research paper have demonstrated, in a practical fashion, that a multi-layer perceptron (MLP) neural network (NN) can be appropriately trained to detect and identify the sudden disconnection of a generation unit in a multi-synchronous generation unit power system. Synthetic time-series bus voltage angles considering low inertia scenarios in the IEEE 39 bus system were used to train the MLP NN. The training process is speeded up by using four GPUs hardware. The simulations results have confirmed the successful detection and identification of the generator outage.
Original language | English |
---|---|
Title of host publication | Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) |
Publisher | IEEE |
Pages | 424-429 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-6654-8240-0 |
ISBN (Print) | 978-1-6654-8240-0 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) - Anchorage, United States Duration: 1 Jun 2022 → 3 Jun 2022 Conference number: 31st |
Publication series
Name | IEEE International Symposium on Industrial Electronics |
---|---|
Volume | 2022-June |
Conference
Conference | 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) |
---|---|
Country/Territory | United States |
City | Anchorage |
Period | 1/06/22 → 3/06/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Artificial neural network
- deep learning
- machine learning
- outage detection and identification
- power system dynamics