Publication

Neural network applications in power systems

Alain Germond
1993
Conference Papers

Abstract

The first part of the presentation is a brief survey of the neural network models and their use in power systems, based on an analysis of more than 200 publications. The second part discusses the applications in static security assessment, with supervised and unsupervised neural network models. An example of static security assessment with the Kohonen classifier is developed: the Kohonen network is trained with vectors representing simulated states of the power system, with or without contingencies. The Kohonen map is used to assess unknown power system states in real time. The two approaches, supervised and unsupervised, are compared and finally, the perspective of industrial implementation is evaluated

Official source

https://infoscience.epfl.ch/entities/publication/c1c67859-4310-44b7-b53c-518c229b684c

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Alain Germond
1993
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c1c67859-4310-44b7-b53c-518c229b684c

About this result

Related concepts (26)

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.

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.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Related publications (26)

Neural network applications in power systems

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