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Neural Network Computing for the Electric Power Industry : Proceedings of the 1992 Inns Summer Workshop.

Author: Dejan J Sobajic
Publisher: Hoboken : Taylor and Francis, 2013.
Series: INNS Series of Texts, Monographs, and Proceedings Series.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
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  Read more...
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Genre/Form: Electronic books
Conference papers and proceedings
Congresses
Additional Physical Format: Print version:
Sobajic, Dejan J.
Neural Network Computing for the Electric Power Industry : Proceedings of the 1992 Inns Summer Workshop.
Hoboken : Taylor and Francis, ©2013
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Dejan J Sobajic
ISBN: 9781134781904 1134781903
OCLC Number: 852757630
Notes: A case study of neural network application: power equipment failure diagnosis.
Description: 1 online resource (237 pages).
Contents: Cover; NEURAL NETWORK COMPUTING FOR THE ELECTRIC POWER INDUSTRY: PROCEEDINGS OF THE 1992 INNS SUMMER WORKSHOP; Copyright; PROORAM COMMITTEE; TABLE OF CONTENTS; FOREWORD; A . Perspectives; LEARNING AND GENERALIZATION CHARACTERISTICS OF THE RANDOM VECTOR FUNCTIONAL-LINK NET; Artificial Neural Networks and Expert Systems in the Power System Operation Environment; A Utility Perspective on Neural Networks, Fuzzy Logic, and Artificial Intelligence; B . Neural Network Methodologies; Backpropagation and its Applications. Using Flow Graph Interreciprocity to Relate Recurrent-Backpropagation and Backpropagation-Through-TimeNeural Network Based Inferential Sensing and Instrumentation; OPTIMIZING NEURAL NETWORKS WITH GENETIC ALGORITHMS; C. Nuclear Power Plants; POTENTIAL USE OF NEURAL NETWORKS IN NUCLEAR POWER PLANTS; Sensor Validation in Power Plants Using Neural Networks; MEASURING FUZZY VARIABLES IN A NUCLEAR REACTOR USING ARTIFICIAL NEURAL NETWORKS; Application of a Real Time Artificial Neural Network for Classifying Nuclear Power Plant Transient Events; Control Rod Wear Recognition Using Neural Nets. SAMSON Severe Accident Management System Online NetworkD . Power System Operation; Comparison of Dynamic Load Models Extrapolation Using Neural Networks and Traditional Methods; On Neural Network Voltage Assessment; NEURAL-NET SYNTHESIS OF TANGENT HYPERSURFACES FOR TRANSIENT SECURITY ASSESSMENT OF ELECTRIC POWER SYSTEMS; POWER SYSTEM STATIC SECURITY ASSESSMENT USING THE KOHONEN NEURAL NETWORK CLASSIFIER; Voltage Stability Monitoring with Artificial Neural Networks; INTELLIGENT LOAD SHEDDING; CONSIDERATION IN INTELLIGENT ALARM PROCESSING; E. Modeling and Prediction. PREDICTIVE SECURITY MONITORING WITH NEURAL NETWORKSEmpirical Modeling in Power Engineering Using the Recurrent Multilayer Perceptron Network; Modeling and Identification with Neural Networks; Autoregressive Neural Network Prediction: Learning Chaotic Time Series and Attractors.; F. Control; Neural Control Systems; POTENTIAL USES OF INTELLIGENT AND ADAPTIVE CONTROLS FOR ELECTRIC POWER SYSTEM OPERATIONS IN THE YEAR 2000 AND BEYOND; Load-Frequency Control Using Neural Networks.; REINFORCEMENT LEARNING FOR ADAPTIVE CONTROL; G . Load Forecasting. APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO LOAD FORECASTINGSHORT-TERM ELECTRIC LOAD FORECASTING USING NEURAL NETWORKS; LOAD FORECASTING BY HIERARCHICAL NEURAL NETWORKS THAT INCORPORATE KNOWN LOAD CHARACTERISTICS; H. Scheduling and Optimization; A Solution Method for Maintenance Scheduling of Thermal Units by Artificial Neural Networks; GENERATION DISPATCH ALGORITHM COORDINATING ECONOMY AND STABILTY BY USING ARTIFICIAL NEURAL NETWORK; I. Fault Diagnosis; IMPULSE TEST FAULT DIAGNOSIS ON POWER TRANSFORMERS USING KOHONEN'S SELF-ORGANIZING NEURAL NETWORK.
Series Title: INNS Series of Texts, Monographs, and Proceedings Series.

Abstract:

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.

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