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Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop Book

Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop
Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop, 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 understandi, Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop has a rating of 3 stars
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Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop, 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 understandi, Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop
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  • Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop
  • Written by author Sobajic
  • Published by Erlbaum, Lawrence Associates, Inc., May 1993
  • 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 understandi
  • 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 understandi
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Book Categories

Authors

Program Committee
Foreword
APerspectives
Learning and Generalization Characteristics of the Random Vector Functional-Link Net3
Artificial Neural Networks and Expert Systems in the Power System Operation Environment11
A Utility Perspective on Neural Networks, Fuzzy Logic, and Artificial Intelligence15
BNeural Network Methodologies
Backpropagation and Its Applications21
Using Flow Graph Interreciprocity to Relate Recurrent-Backpropagation and Backpropagation-Through-Time31
Neural Network Based Inferential Sensing and Instrumentation37
Optimizing Neural Networks Using Genetic Algorithms41
CNuclear Power Plants
Potential Use of Neural Networks in Nuclear Power Plants47
Sensor Validation in Power Plants Using Neural Networks51
Measuring Fuzzy Variables in a Nuclear Reactor Using Artificial Neural Networks55
Application of a Real Time Artificial Neural Network for Classifying Nuclear Power Plant Transient Events59
Control Rod Wear Recognition Using Neural Nets63
Severe Accident Management System On-Line Network (SAMSON)69
DPower System Operation
Comparison of Dynamic Load Models Extrapolation Using Neural Networks and Traditional Methods77
On Neural Network Voltage Assessment81
Neural Network Synthesis of Tangent Hypersurfaces for Transient Security Assessment of Electric Power Systems87
Power System Static Security Assessment Using the Kohonen Neural Network Classifier93
Voltage Stability Monitoring with Artificial Neural Networks101
Intelligent Load Shedding107
Considerations in Intelligent Alarm Processing111
EModeling and Prediction
Predictive Security Monitoring with Neural Networks117
Empirical Modeling in Power Engineering Using the Recurrent Multilayer Perceptron Network123
Modeling and Identification with Neural Networks129
Autoregressive Neural Network Prediction: Learning Chaotic Time Series and Attractors135
FControl
Neural Control Systems143
Potential Uses of Intelligent and Adaptive Controls for Electric Power System Operations in the Year 2000 and Beyond149
Load-Frequency Control Using Neural Networks153
Reinforcement Learning for Adaptive Control159
GLoad Forecasting
Application of Artificial Neural Networks to Load Forecasting165
Short-Term Electric Load Forecasting Using Neural Networks173
Load Forecasting by Hierarchical Neural Networks that Incorporate Known Load Characteristics179
HScheduling and Optimization
A Solution Method for Maintenance Scheduling of Thermal Units by Artificial Neural Networks185
Generation Dispatch Algorithm Coordinating Economy and Stability by Using Artificial Neural Netoworks191
IFault Diagnosis
Impulse Test Fault Diagnosis on Power Transformers Using Kohonen's Self-Organizing Neural Network199
A Case Study of Neural Network Application: Power Equipment Application Failure207
Integrating Neural Networks with Influence Diagrams for Power Plant Monitoring and Diagnostics213
Use of Neural Network in Optimizing RPV Bolting Procedures217
1993 INNS Board of Governors
INNS Fact Sheet
1993 INNS Membership Application


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Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop, 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 understandi, Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop

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Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop, 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 understandi, Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop

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Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop, 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 understandi, Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop

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