Power flow classification for static security assessment

The authors investigate 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, 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

Published in:
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems (Cat. No.91TH0374-9), 83 - 8
power flow classification;static security assessment;artificial neural network;Kohonen's self-organizing feature map;power system static security;real-time;N-dimensional input vectors;M neurons;synaptic weight vectors;learning algorithm;

 Record created 2007-04-04, last modified 2018-03-18

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