Communications and control for electric power systems: Power flow classification for static security assessment
Abstract
This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.
- Publication:
-
NASA STI/Recon Technical Report N
- Pub Date:
- February 1993
- Bibcode:
- 1993STIN...9413948N
- Keywords:
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- Classifications;
- Electric Power Supplies;
- Neural Nets;
- Power Transmission;
- Real Time Operation;
- Self Organizing Systems;
- Algorithms;
- Computerized Simulation;
- Electronics and Electrical Engineering