Application of Improved Process Neural Network Based on the Fireworks Algorithm in the Temperature-Rise Predictions of a Large Generator Rotor

Abstract

Building an effective algorithm model for large key power equipment has very important research significance and application value. Aiming at the typical operating state characteristics of large generators and taking the temperature changes as the main research indicators, the improved fireworks algorithm was used to optimize the process neural network, and the key data characteristics were studied based on the machine experiment and actual operation data of a 300 MW generator so as to find the variation and development trends of the maximum temperature rise caused by negative-sequence current. Furthermore, the effectiveness of the neural network model suitable for large generators established in this paper was verified by test functions and experiments. On this basis, the calculation method was applied to different working conditions, component materials, and heating positions of the generator. Moreover, the temperature-rise prediction results of the structural components for the generator rotor were obtained, and the optimization scheme of the slot wedge material given, which provide a reference for temperature-rise research and the selection of component materials for large generators.