Application of BP neural network in turbo-generator harmonic analysis under negative-sequence loss conditions

This work is the application of back propagation neural network (BP NN) on the world´s first AP1000 third generation 1250 MVA nuclear half-speed (4-pole) turbo-generator in harmonic analysis. Large-capacity generator is the mainstream of nuclear power future, which leads the trend of nuclear power development and also faces many problems need to be tackled as soon as possible. Harmonic distortion is a predominant factor influent turbo-generator´s output power quality and power system operations. In order to solve the problem, this paper presents an application control strategy to estimate the harmonics in the AP1000 nuclear turbo-generator unit using BP neural network, by which the neural structure can be used for harmonic analysis and power quality control. Simulation results prove that the method can realize the ability of self-learning. Meanwhile, the application results identify that BP neural network is an effective technology to calculate and analyze the harmonics in AP1000 large-capacity generator system under various negative-sequence loss conditions, and pave the path of the further theory research and the application practice a good solid foundation at the same time.