Power Systems Contingency Analysis using Artificial Neural Networks
[C56] Semitekos D, Avouris N., Power Systems Contingency Analysis using Artificial Neural Networks, Proc. CSIT 2002, Patras, September 2002. (pdf)

Contingency analysis and risk assessment are important tasks for the safe operation of electrical energy networks.During the steady state study of an electrical network any one of the possible contingencies can have either no effect, or serious effect, or even fatal results for the network safety, depending on a given network operating state.
Load flow analysis can be used as a crisp technique for contingency risk assessment. However performing at run time the necessary load flow analysis studies is a tedious and time consuming operation. An alternative solution is the off-line training and the run-time application of artificial neural networks.
This article aims at describing how artificial neural networks can be used to bypass the traditional load flow cycle, resulting in significantly faster computation times for online contingency analysis. A discussion over the efficiency of the proposed techniques is also included.

Computer-supported collaborative learning has been an active area of research since the beginning for the HCI group more>>

Web usability team of the HCI Group has been active in studying human-web interaction and ways to support the design of accessible, findable, usable and aesthetically appealing web sites. more>>

Mobile Technology Unit of the HCI Group has been studying design and evaluation of mobile applicationss more>>

Hci Group | Electrical and Computer Engineering | University of Patras