원문정보
초록
영어
The research is aimed at 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. Harmonic distortion has caused many power quality problems in the large-capacity generator unite of power system since the discovery of alternating current system. Taken AP1000 nuclear turbo-generator which has many problems needed solving as the analysis example, this paper presents the BP neural network technique which can realize the ability of self-learning to estimate and calculate the harmonics in AP1000 turbo-generator under various negative-sequence loss conditions. Considering the BP neural network application and harmonic effects in the large-capacity turbo-generator, overall simulation is performed in this work. Simulation results indicate that the method of using BP neural network is effective and accurate, and the change regulation of each harmonic is also obtained.
목차
1. Introduction
2. Basic Principles of AP1000 Generator Harmonic Analysis
2.1. AP1000 turbo-generator
2.2. Harmonic production in Turbo-generator operations
2.3. Harmonic analysis under the rated condition
3. Mature Trained BP Neural Network
3.1. BP NN application model
3.2. BP NN Model Training
4. BP NN Model Application and Results
4.1. Terminal voltage
4.2. Stator current
4.3. Excitation current
5. Conclusions
Acknowledgements
References