Abstract
A neural-network-aided solution to the problem of static-security assessment of a large scale power system is proposed. It is based on a pattern-recognition technique where a group of neural networks is trained to classify the secure/insecure status of the power system for specific contingencies based on the precontingency system variables. The large dimensionality of the input data is reduced by partitioning the problem into smaller subproblems at different stages. When each trained NN is queried online, it can provide the power-system operator with the security status of the current operating point for a specified contingency. Parallel network architecture and the adaptive capability of the neural networks can be combined to achieve high speeds of execution and good classification accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEE Proceedings C Generation, Transmission and Distribution
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
