Research on Fault Diagnosis of Thermal Power System of Thermal Power Unit under Different Loads

Abstract

Aiming at the problem of difficulty in acquiring fault data under different loads, a domain-adversarial-based fault diagnosis method for convolutional neural networks is established by combining convolutional neural networks with adversarial domain adaptive methods. The method uses convolutional neural networks to extract the fault features of the source and target domain samples, and simultaneously implements joint adjustment of the distributions of the domain-level and class-level features, aligns the feature distributions of the source and target domains by minimizing the difference in the distributions of the two domains through adversarial learning of the domains and aligns the feature distributions of each class of faults by making the distributions of the same kind of features closer through the loss of the central discriminant. With the help of a 600MW supercritical thermal power unit full-working condition simulation system, experiments are carried out under 100%, 90%, 80% and 70% of the rated load, and the results show that the method has effectiveness and superiority in the fault diagnosis of thermal system under different loads.