Power Plant Surveillance and Diagnostics: Applied Research with Artificial Intelligence

In little more than a decade, the availability of nuclear power plants in the United States and most European and Pacific Rim countries has increased dramatically­ from around 60% to about 90%. Such improvement is equivalent to adding approximately 30,000 MW e of new generating capacity in this period in the United States and perhaps an equal amount elsewhere. Some of this improvement has been due to longer fuel cycles-increases from 12 months to 18 or 24 months, but the major gains have been due to on-line maintenance and improved operation and planning by the utilities, accomplished primarily by training of plant operators and support personnel. However, there are emerging indications that the benefits of training are asymptotically approaching the limit beyond which it cannot improve availability and that plant personnel errors may also be approaching a comparable lower limit. Indeed, further improvement appears to be dependent upon the effective introduction and use of plant operational support systems. The Halden Reactor Project (HRP) historically has focused on plant operational support system, i. e. , such tools as COPMA (Computerized OPeration MAnuals), PEANO (Process Evaluation and Analysis by Neural Operators), TEMPO (ThErMal Performance and Optimization), PLASMA (PLAnt Safety Monitoring and Assessment system), and others.

Table of Contents:
1 Modern Approaches and Advanced Applications for Plant Surveillance and Diagnostics: An Overview.- 2 Regulatory Treatment of On-line Surveillance and Diagnostic Systems.- 3 Optimized Maintenance and Management of Ageing of Critical Equipment in Nuclear Power Plants.- 4 Overview of Recent KFM AEKI Activities in the Field of Plant Surveillance and Diagnostics.- 5 Adaptive Model-Based Control of Non-linear Plants Using Soft Computing Techniques.- 6 Bayesian Networks in Decision Support.- 7 Hidden Markov Model Based Transient Identification in NPPs.- 8 Expert System-Based Implementation of Failure Detection.- 9 Detection of Incipient Signal or Process Faults in a Co-Generation Plant Using the Plant ECM System.- 10 On-Line Determination of the MTC (Moderator Temperature Coefficient) by Neutron Noise and Gamma-Thermometer Signals.- 11 Detecting Impacting of BWR Instrument Tubes by Wavelet Analysis.- 12 Development of Advanced Core Noise Monitoring System for a Boiling Water Reactor.- 13 Diagnosis of Measuring Systems Using Cluster Analysis Applied to Hydrostatic Water Level Measurement.- 14 A Hybrid Fuzzy-Fractal Approach for Time Series Analysis and Prediction and Its Applications to Plant Monitoring.- 15 Failure Detection Using a Fuzzy Neural Network with an Automatic Input Selection Algorithm.- 16 Artificial Neural Networks Modeling as a Diagnostic and Decision Making Tool.- 17 A New Approach for Transient Identification with “Don’t Know” Response Using Neural Networks.- 18 Planning Surveillance Test Policies Through Genetic Algorithms.- 19 A Possibilistic Approach for Transient Identification with “Don’t Know” Response Capability Optimized by Genetic Algorithm.- 20 Regularization of Ill-Posed Surveillance and Diagnostic Measurements.- 21 Application ofNeuro-Fuzzy Logic for Early Detection and Diagnostics in Gas Plants and Combustion Chambers at ENEA.- 22 ALADDIN: Event Recognition & Fault Diagnosis for Process & Machine Condition Monitoring.- 23 PEANO and On-Line Monitoring Techniques for Calibration Reduction of Process Instrumentation in Power Plants.