A perspective on use of neural-net computing in training simulator design

Abstract

The authors explore and demonstrate the feasibility of combined artificial intelligence/neural-net methodology for carrying out dynamic power system analysis in real-time. This methodology will be capable of characterizing the near term transient stability of the system, as well as perform mid-term and long term dynamic security analyses. In the transient stability analysis, the authors are principally concerned with a question whether the system can return to the steady state. In the mid-term and long-term-security analysis, they are also concerned with a manner in which the final steady state is reached, whether system performance constraints are violated on the way and whether further protective actions might be triggered unexpectedly with undesired actions.<>