About the Book
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.
Table of Contents:
Contents: D. Sobajic, Foreword. Part I:Perspectives.Y-H. Pao, G-H. Park, Learning and Generalization Characteristics of the Random Vector Functional-Link Net. C-C. Liu, M. Damborg, Artificial Neural Networks and Expert Systems in the Power System Operation Environment. E. Bradley, A Utility Perspective on Neural Networks, Fuzzy Logic, and Artificial Intelligence. Part II:Neural Network Methodologies.B. Widrow, M.A. Lehr, Backpropagation and Its Applications. F. Beaufays, E.A. Wan, Using Flow Graph Interreciprocity to Relate Recurrent-Backpropagation and Backpropagation-Through-Time. A. Guha, Neural Network Based Inferential Sensing and Instrumentation. S.A. Harp, T. Samad, Optimizing Neural Networks Using Genetic Algorithms. Part III:Nuclear Power Plants.R. Uhrig, Potential Use of Neural Networks in Nuclear Power Plants. M. Khadem, A. Ipakchi, F.J. Alexandro, R.W. Colley, Sensor Validation in Power Plants Using Neural Networks. A. Ikonomopoulos, L. Tsoukalas, R. Uhrig, Measuring Fuzzy Variables in a Nuclear Reactor Using Artificial Neural Networks. Y.D. Lukic, C.R. Stevens, J. Si, Application of a Real Time Artificial Neural Network for Classifying Nuclear Power Plant Transient Events. J.A. Boshers, C.H.M. Saylor, S. Kamadolli, R. Wood, C. Isik, Control Rod Wear Recognition Using Neural Nets. R. Doremus, Severe Accident Management System On-Line Network (SAMSON). Part IV:Power System Operation.H. Ren-mu, A.J. Germond, Comparison of Dynamic Load Models Extrapolation Using Neural Networks and Traditional Methods. B. Avramovic, On Neural Network Voltage Assessment. D. Sobajic, Y-H. Pao, M. Djukanovic, Neural Network Synthesis of Tangent Hypersurfaces for Transient Security Assessment of Electric Power Systems. D. Niebur, A.J. Germond, Power System Static Security Assessment Using the Kohonen Neural Network Classifier. H. Mori, Voltage Stability Monitoring with Artificial Neural Networks. D. Novosel, A.B. Boveri, R.L. King, Intelligent Load Shedding. E. Chan, N. Markushevich, R. Adapa, Considerations in Intelligent Alarm Processing. Part V:Modeling and Prediction.D.J. Sobajic, Y-H. Pao, D.T. Lee, Predictive Security Monitoring with Neural Networks. A.G. Parlos, A.D. Patton, Empirical Modeling in Power Engineering Using the Recurrent Multilayer Perceptron Network. T. Samad, Modeling and Identification with Neural Networks. E. Wan, Autoregressive Neural Network Prediction: Learning Chaotic Time Series and Attractors. Part VI:Control.B. Widrow, F. Beaufays, Neural Control Systems. R.L. King, M.L. Oatts, Potential Uses of Intelligent and Adaptive Controls for Electric Power System Operations in the Year 2000 and Beyond. F. Beaufays, B. Widrow, Load-Frequency Control Using Neural Networks. L.L. Adams, Reinforcement Learning for Adaptive Control. Part VII:Load Forecasting.A.J. Germond, N. Macabrey, T. Baumann, Application of Artificial Neural Networks to Load Forecasting. M. Khadem, A. Lago, E. Dobrowolski, Short-Term Electric Load Forecasting Using Neural Networks. J.Y. Cheung, J. Fagan, D.C. Chance, Load Forecasting by Hierarchical Neural Networks that Incorporate Known Load Characteristics. Part VIII:Scheduling and Optimization.H. Sasaki, Y. Takiuchi, J. Kubokawa, A Solution Method for Maintenance Scheduling of Thermal Units by Artificial Neural Networks. H. Saitoh, Y. Shimotori, J. Toyoda, Generation Dispatch Algorithm Coordinating Economy and Stability by Using Artificial Neural Networks. Part IX:Fault Diagnosis.T. Baumann, A.J. Germond, D. Tschudi, Impulse Test Fault Diagnosis on Power Transformers Using Kohonen's Self-Organizing Neural Network. Y. Du, F. Wang, T.C. Cheng, A Case Study of Neural Network Application: Power Equipment Application Failure. A. Agogino, M-L. Tseng, P. Jain, Integrating Neural Networks with Influence Diagrams for Power Plant Monitoring and Diagnostics. W.L. Biach, Use of Neural Network in Optimizing RPV Bolting Procedures.
