Optimal Training Algorithm Application to Design an Associative Memory for Fault Diagnosis at a Fossil Electric Power Plant

Abstract

This paper presents the development and application of a neural networks-based scheme for fault diagnosis, including detection and classification, for the steam generator of a fossil electric power plant. The scheme is constituted by two components: residual generation and fault classification. The first component generates residuals via the difference between measurements coming from the plant and a neural network predictor. The neural network predictor is trained with healthy data collected from a full-scale simulator reproducing reliably the process behavior. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. The fault patterns are stored in an associative memory based on a recurrent neural network which is trained via optimal training algorithm. The scheme is evaluated on-line via a full-scale simulator to diagnose the main faults appearing in this kind of power plants when load power is constant and when the operator is carrying out load power changes. Furthermore, the fault diagnosis strategy is be able to distinguish when the operator is carrying out load power changes free of fault as normal operating conditions.