book

Neural Network Computing for the Electric Power Industry

by Dejan J. Sobajic

June 2013

Intermediate to advanced

240 pages

8h 42m

English

Psychology Press

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Learning and Generalization Characteristics of the Random Vector Functional-Link NetArtificial Neural Networks and Expert Systems in the Power System Operation EnvironmentA Utility Perspective on Neural Networks, Fuzzy Logic, and Artificial Intelligence
Backpropagation and Its ApplicationsUsing Flow Graph Interreciprocity to Relate Recurrent-Backpropagation and Backpropagation-Through-TimeNeural Network Based Inferential Sensing and InstrumentationOptimizing Neural Networks Using Genetic Algorithms
Potential Use of Neural Networks in Nuclear Power PlantsSensor Validation in Power Plants Using Neural NetworksMeasuring Fuzzy Variables in a Nuclear Reactor Using Artificial Neural NetworksApplication of a Real Time Artificial Neural Network for Classifying Nuclear Power Plant Transient EventsControl Rod Wear Recognition Using Neural NetsSevere Accident Management System On-Line Network (SAMSON)

Comparison of Dynamic Load Models Extrapolation Using Neural Networks and Traditional MethodsOn Neural Network Voltage AssessmentNeural Network Synthesis of Tangent Hypersurfaces for Transient Security Assessment of Electric Power SystemsPower System Static Security Assessment Using the Kohonen Neural Network ClassifierVoltage Stability Monitoring with Artificial Neural NetworksIntelligent Load SheddingConsiderations in Intelligent Alarm Processing
Predictive Security Monitoring with Neural NetworksEmpirical Modeling in Power Engineering Using the Recurrent Multilayer Perceptron NetworkModeling and Identification with Neural NetworksAutoregressive Neural Network Prediction: Learning Chaotic Time Series and Attractors
Neural Control SystemsPotential Uses of Intelligent and Adaptive Controls for Electric Power System Operations in the Year 2000 and BeyondLoad-Frequency Control Using Neural NetworksReinforcement Learning for Adaptive Control
Application of Artificial Neural Networks to Load ForecastingShort-Term Electric Load Forecasting Using Neural NetworksLoad Forecasting by Hierarchical Neural Networks that Incorporate Known Load Characteristics
A Solution Method for Maintenance Scheduling of Thermal Units by Artificial Neural NetworksGeneration Dispatch Algorithm Coordinating Economy and Stability by Using Artificial Neural Netoworks
Impulse Test Fault Diagnosis on Power Transformers Using Kohonen’s Self-Organizing Neural NetworkA Case Study of Neural Network Application: Power Equipment Application FailureIntegrating Neural Networks with Influence Diagrams for Power Plant Monitoring and DiagnosticsUse of Neural Network in Optimizing RPV Bolting Procedures
INNS Fact Sheet1993 INNS Membership Application

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.