Publication

Unsupervised neural net classification of power system static security states

Alain Germond
1992
Journal Articles

Abstract

The authors study the application of Kohonen's self-organizing feature map to power system static security assessment. The Kohonen classifier maps vectors of an N-dimensional space to a two-dimensional neural net in a nonlinear way, preserving the topological order of the vectors which, in general, is not known a priori. The classification of line-loading patterns by the Kohonen network is demonstrated for two different test systems. The generalization capability of the Kohonen network permits the correct classification of system states which have not been encountered during the training phase. This feature is extremely important for power system operation where it is unrealistic to expect that all possible cases will be encountered during off-line simulation

Official source

https://infoscience.epfl.ch/entities/publication/42636f23-04d8-4a2c-a622-e89d7a21c9f4

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Alain Germond
1992
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/42636f23-04d8-4a2c-a622-e89d7a21c9f4

About this result

Related concepts (32)

Self-organizing map

A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. For example, a data set with variables measured in observations could be represented as clusters of observations with similar values for the variables.

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Related publications (32)

Unsupervised neural net classification of power system static security states

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