Tutorial Course on Artificial Neural Networks with Applications to Power Systems (1996) ? PDF

From the Course Editors In recent years new technologies have been introduced to help the engineer analyze and design large-scale complex systems. While no single technique has emerged as widely applicable to the engineering of such systems, the new tools are recognized as part of the necessary arsenal of the modern engineer. One of these techniques is artificial neural network technology, whose primary advantages are in the areas of learning algorithms; on-line adaptation of dynamic systems; quick parallel computation; and intelligent interpolation of data. The purpose of this course is to provide an introduction to artificial neural network (ANN) technology for power system engineers. The tutorial is composed of two parts: The first part gives an overview of ANNs, including network architectures, principles of operation, learning rules, advantages and limitations. The objective is to give the readers a working knowledge of ANN including examples. The second part of the tutorial deals with specific applications of ANN to power system problems, such as load forecasting, security assessment, planning, fault diagnosis and control. Artificial neural networks represent a growing new technology as indicated by the wide variety of the proposed applications (e.g. remote sensing, control, forecasting, pattern recognition) and by the development of ANN integrated circuits and hardware modules. The main reasons for this growing activity are the ability of ANNs to learn complex nonlinear relations, and their modular structure which allows parallel processing. Neural networks have been shown to be useful in solving algorithmic type problems and more importantly, to tackle problems for which algorithms are not available but significant data is available. The tutorial will emphasize practical aspects of ANN design, namely, the selection of the ANN architecture for a particular application; the ANN training requirements (in terms of the selection of a training set and of a learning scheme); the setting up of the input data (i.e. scaling), and performance evaluation. This goal will be accomplished by presenting and analyzing several case studies. The coordinators of this tutorial would like to thank Professor Robert Fischl for his involvement during the early stages of the tutorial planning. Mohamed A. El-Sharkawi and Dagmar Niebur Download Full Version