Automatic optimization method for EAST-NBI beam extraction experiment parameters based on neural network

Abstract

As one of the most important auxiliary heating facilities of tokamak, Neutral Beam Injector (NBI) has the highest heating efficiency and clearest operating mechanism. Almost all important tokamak devices in the world are equipped with NBI, including Experimental Advanced Superconducting Tokamak (EAST). The mismatch of the operating parameters of the NBI ion source will lead to the instability of the plasma, and maybe cause breakdown in the ion source, which will limit the operation of the NBI long pulse and high power, and it will even be difficult to heat the plasma in the tokamak. In order to stabilize the plasma in the NBI ion source, data from multiple rounds of experiments along with a priori information obtained from a predictive plasma model are used. This paper proposes a method based on Self-Organizing Map (SOM) and Back Propagation(BP) type neural network to estimate the pulse width during the beam extraction process of the NBI ion source under given parameters by training historical data, and adjust the operating parameters accordingly to reduce the ratio of ion source breakdown. The SOM approach mainly relies on current and voltage sensors's data instead of a priori information, and tries to estimate the beam extraction pulse width with less calculations. A BP neural network is also designed to reduce the uncertainty of the SOM algorithm. The algorithms have been tested on off-line data, obtained from experimental shots at EAST-NBI, Hefei, China.