Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 Inns Summer Workshop [NOOK Book]

Overview

Power system computing with neural networks is one of the fastest growing fields in the history of power system engineering. Since 1988, a considerable amount of work has been done in investigating computing capabilities of neural networks and understanding their relevance to providing efficient solutions for outstanding complex problems of the electric power industry. A principal objective of a power utility is to provide electric energy to its customers in a secure, reliable and economic manner. Toward this ...
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Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 Inns Summer Workshop

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Overview

Power system computing with neural networks is one of the fastest growing fields in the history of power system engineering. Since 1988, a considerable amount of work has been done in investigating computing capabilities of neural networks and understanding their relevance to providing efficient solutions for outstanding complex problems of the electric power industry. A principal objective of a power utility is to provide electric energy to its customers in a secure, reliable and economic manner. Toward this aim, utility personnel are engaged in a variety of activities in areas of supervisory control and monitoring, evaluation of operating conditions, operation planning and scheduling, system development, equipment testing, etc. Over the past decades significant advances have been made in the development of new concepts, design of hardware and software systems, and implementation of solid-state devices which all contributed to the steadily improving power system performance that we are experiencing today. Advanced information processing technologies played an important role in these development efforts.

Members of the Special Interest Group for Power Engineering of the INNS recognized the need for bringing together leading researchers in the field of neurocomputing with experts from power utilities and manufacturing companies to assess the current state of affairs and to explore the directions of further research and practice. This book is based on The Summer Workshop on Neural Network Computing for the Electric Power Industry which brought together approximately forty specialists with backgrounds in power engineering, system operation and planning, neural network theory and AI systems design. An informal and highly inspiring atmosphere of the workshop facilitated open discussion and exchange of expertise between the participants.
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Product Details

Table of Contents

Program Committee
Foreword
A Perspectives
Learning and Generalization Characteristics of the Random Vector Functional-Link Net 3
Artificial Neural Networks and Expert Systems in the Power System Operation Environment 11
A Utility Perspective on Neural Networks, Fuzzy Logic, and Artificial Intelligence 15
B Neural Network Methodologies
Backpropagation and Its Applications 21
Using Flow Graph Interreciprocity to Relate Recurrent-Backpropagation and Backpropagation-Through-Time 31
Neural Network Based Inferential Sensing and Instrumentation 37
Optimizing Neural Networks Using Genetic Algorithms 41
C Nuclear Power Plants
Potential Use of Neural Networks in Nuclear Power Plants 47
Sensor Validation in Power Plants Using Neural Networks 51
Measuring Fuzzy Variables in a Nuclear Reactor Using Artificial Neural Networks 55
Application of a Real Time Artificial Neural Network for Classifying Nuclear Power Plant Transient Events 59
Control Rod Wear Recognition Using Neural Nets 63
Severe Accident Management System On-Line Network (SAMSON) 69
D Power System Operation
Comparison of Dynamic Load Models Extrapolation Using Neural Networks and Traditional Methods 77
On Neural Network Voltage Assessment 81
Neural Network Synthesis of Tangent Hypersurfaces for Transient Security Assessment of Electric Power Systems 87
Power System Static Security Assessment Using the Kohonen Neural Network Classifier 93
Voltage Stability Monitoring with Artificial Neural Networks 101
Intelligent Load Shedding 107
Considerations in Intelligent Alarm Processing 111
E Modeling and Prediction
Predictive Security Monitoring with Neural Networks 117
Empirical Modeling in Power Engineering Using the Recurrent Multilayer Perceptron Network 123
Modeling and Identification with Neural Networks 129
Autoregressive Neural Network Prediction: Learning Chaotic Time Series and Attractors 135
F Control
Neural Control Systems 143
Potential Uses of Intelligent and Adaptive Controls for Electric Power System Operations in the Year 2000 and Beyond 149
Load-Frequency Control Using Neural Networks 153
Reinforcement Learning for Adaptive Control 159
G Load Forecasting
Application of Artificial Neural Networks to Load Forecasting 165
Short-Term Electric Load Forecasting Using Neural Networks 173
Load Forecasting by Hierarchical Neural Networks that Incorporate Known Load Characteristics 179
H Scheduling and Optimization
A Solution Method for Maintenance Scheduling of Thermal Units by Artificial Neural Networks 185
Generation Dispatch Algorithm Coordinating Economy and Stability by Using Artificial Neural Netoworks 191
I Fault Diagnosis
Impulse Test Fault Diagnosis on Power Transformers Using Kohonen's Self-Organizing Neural Network 199
A Case Study of Neural Network Application: Power Equipment Application Failure 207
Integrating Neural Networks with Influence Diagrams for Power Plant Monitoring and Diagnostics 213
Use of Neural Network in Optimizing RPV Bolting Procedures 217
1993 INNS Board of Governors
INNS Fact Sheet
1993 INNS Membership Application
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