Communications and control for electric power systems: Power flow classification for static security assessment
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 the paper, 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.
- Research Organization:
- California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab. (JPL)
- Sponsoring Organization:
- USDOE, Washington, DC (United States); National Aeronautics and Space Administration, Washington, DC (United States); Ecole Polytechnique Federale, Lausanne (Switzerland)
- DOE Contract Number:
- AI05-79ET29372
- OSTI ID:
- 10169859
- Report Number(s):
- DOE/ET/29372-T1; JPL-92; ON: DE93018317
- Resource Relation:
- Other Information: PBD: Feb 1993
- Country of Publication:
- United States
- Language:
- English
Similar Records
An introduction to neural networks: A tutorial
Properties and characteristics of self-organizing neural networks for unsupervised pattern recognition