Abstract
| Original language | English |
|---|---|
| Publication status | Published - 2003 |
| Event | 38th International Universities Power Engineering Conference - Aristotle University of Thessaloniki, Thessaloniki, Greece Duration: 1 Sep 2003 → 3 Sep 2003 |
Conference
| Conference | 38th International Universities Power Engineering Conference |
|---|---|
| Abbreviated title | UPEC 2003 |
| Country | Greece |
| City | Thessaloniki |
| Period | 1/09/03 → 3/09/03 |
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Investigation into the application of artificial neural networks for detecting loss of mains conditions. / Salman, S. K.; King, David J.
2003. Abstract from 38th International Universities Power Engineering Conference, Thessaloniki, Greece.Research output: Contribution to conference › Abstract
TY - CONF
T1 - Investigation into the application of artificial neural networks for detecting loss of mains conditions
AU - Salman, S. K.
AU - King, David J.
PY - 2003
Y1 - 2003
N2 - With the increase of number of embedded generators (EGs) in public utility networks the need for adequate protection schemes has become extremely important. In particular, safety problems that arise due to loss of mains (LOM), and the inability of current protection, under certain circumstances, to detect it means that there is a lot of current research into possible alternative methods for detecting LOM. Also, the use of artificial intelligence (AI) in power system operation is becoming widespread. In particular, artificial neural networks (ANN) are being developed to tackle a number of power system control and protection problems. One of the most important things to consider when using ANN is deciding upon appropriate and relevant training/testing data. This paper results from a project to find a new method of detection of LOM for EG using ANN, and outlines the methods used to develop suitable training/testing data
AB - With the increase of number of embedded generators (EGs) in public utility networks the need for adequate protection schemes has become extremely important. In particular, safety problems that arise due to loss of mains (LOM), and the inability of current protection, under certain circumstances, to detect it means that there is a lot of current research into possible alternative methods for detecting LOM. Also, the use of artificial intelligence (AI) in power system operation is becoming widespread. In particular, artificial neural networks (ANN) are being developed to tackle a number of power system control and protection problems. One of the most important things to consider when using ANN is deciding upon appropriate and relevant training/testing data. This paper results from a project to find a new method of detection of LOM for EG using ANN, and outlines the methods used to develop suitable training/testing data
M3 - Abstract
ER -