Artificial Neural Networks in the Power Industry, Survey and Applications
The paper presents a survey of the development and experience with artificial neural net (ANN).
Neural Networks↗
Engineering topics
Niebur, Dagmar
Publications and source records attributed to Niebur, Dagmar.
Application of Neural Networks to Power System Security Assessment and Enhancement
This tutorial presents an overview of the application of artificial neural network (ANN) to power.
Neural Networks↗
An Example of Unsupervised Networks Kohonen's Self-Organizing Feature Map
Kohonen's self-organizing feature map belongs to a class of unsupervised artificial neural network commonly referred to as topographic maps. It serves two purposes, the quantization and dimensionality reduction of date. A short description of its history and its biological context is given. We show that the inherent classification properties of the feature map make it a suitable candidate for solving the classification task in power system areas like load forecasting, fault diagnosis and security assessment.
Kohonen Feature Map↗
Introduction to Concepts in Artificial Neural Networks
This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.
Artificial Neural↗
Artificial Neural Networks
Electric power systems represent complex systems involving many electrical components whoseoperation has to be planned, analyzed, monitored and controlled. The time-scale of tasks in electricpower systems extends from long term planning years ahead to milliseconds in the area of control. The behavior of power systems is highly non-linear. Monitoring and control involves several hundred variables which are only partly available by measurements.
Neural Networks↗