Publication

Power system static security assessment using the Kohonen neural network classifier

Alain Germond
1992
Journal Articles

Abstract

The authors present the application of an artificial neural network, Kohonen's self-organizing feature map, for the classification of power system states. This classifier maps vectors of an N-dimensional space to a two-dimensional neural net in a nonlinear way, preserving the topological order of the input vectors. Therefore, secure operating points-that is, vectors inside the boundaries of the secure domain-are mapped to a different region of the neural map than insecure operating points. The application of this classifier to power system security assessment is presented, and simulation results are discussed

Official source

https://infoscience.epfl.ch/entities/publication/dd961913-d295-48ba-8de8-6dfcc4695422

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Alain Germond
1992
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/dd961913-d295-48ba-8de8-6dfcc4695422

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.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

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.

Related publications (32)

Power system static security assessment using the Kohonen neural network classifier

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