We present an experimental method to obtain neutral beam injection (NBI) power scaling laws with operating parameters of the NBI system on HL-2A, including the beam divergence angle, the beam power transmission efficiency, the neutralization efficiency and so on. With the empirical scaling laws, the estimating power can be obtained in every shot of experiment on time, therefore the important parameters such as the energy confinement time can be obtained precisely. The simulation results by the tokamak simulation code (TSC) show that the evolution of the plasma parameters is in good agreement with the experimental results by using the NBI power from the empirical scaling law.

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Accurate user-transformer connectivity relationship (UTCR) plays a key role in fine management of low-voltage distribution network (LVDN) i.e., load expansion, line loss management, and electrical service restoration after outage. Limited data and low discriminability and noise in data increase the difficulty to identify UTCR for the existing data analytics methods. To overcome these hurdles, this paper proposes a novel UTCR algorithm which combining the data preprocessing with multi-dimensional priori knowledge based on voltage characteristics in LVDN. Firstly, the prior knowledge related to UTCR are refined on account of voltage correlation characteristics of users at different locations to provide theoretical foundation. Then, Z-score and principal component analysis are combined to standardize and extract features from the original voltage data to magnify the differences between data and reduce the impact of data noise. Further, on the basis of the prior knowledge of voltage correlation characteristics, a knowledge-driven identification model is proposed to identify users with wrong UTCR and their real UTCR. Finally, the performance of the proposed algorithm is verified on simulated LVNDs. The comparison analysis between the proposed method and other published methods and the impact of the number of principal components on the identification accuracy are also investigated. The results indicate that the proposed method achieves higher recognition accuracy than other published methods with low discriminability and noise in data.INDEX TERMS User-transformer connectivity relationship identification, low-voltage distribution network, data pre-processing, voltage correlation characteristics, knowledge-driven approaches

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The discharge gas pressure is a key factor to influence the extracted current of ion source. In this paper, the dependence of extracted current on discharge gas pressure was investigated in detail at different arc discharge currents. The discharge gas pressure with a very broad range (0.1 Pa-2.7 Pa) was scanned for the first time. It is turned out that, with the increasing of discharge gas pressure, the extracted current increases and the arc voltage decreases at different arc currents; however, when the discharge gas pressure exceeds a certain value, the extracted current decreases. For the same discharge gas pressure, the higher the arc current, the higher the arc voltage and the extracted current are. The arc efficiency was also calculated, and its dependence on gas pressure was almost the same with the dependence of extracted current on gas pressure, but at the same discharge gas pressure, the lower the arc current, the higher the arc efficiency is and the lower the extracted current is.