An improved approach based on sequential method for the AC-DC power flow calculation is proposed in this paper. This approach solves the convergence problem caused by voltage violations at AC buses during the power flow calculation for the DC subsystems. The convergence property can be significantly improved by adjusting the converter transformer tap position flexibly. In order to adjust the tap position of the converter transformer flexibly, three mainly modifications are proposed. Firstly, the equations for whole DC systems are decoupled into individual DC systems so as to easily figure out which DC system's tap position needs adjustment. Secondly, the tap ratio of a converter transformer is selected as an alternative state variable to replace the cosine of the control angle when necessary. Thirdly, the Newton-Raphson method is utilized to solve DC subsystems instead of the method using the linear equations. Furthermore, a theoretical analysis of the advantages of the proposed approach is also presented. Numerical simulations and practical applications show that the proposed approach meet the requirement of different system operating conditions and has advantages in terms of convergence and speed. The proposed approach has been successfully integrated into the Energy Management System (EMS) for China Southern Power Grid. Index Terms-AC-DC, HVDC transmission, multi-infeed DC systems, Newton-Raphson method, power flow analysis. I. INTRODUCTION C HINA has made remarkable strides in high voltage direct current (HVDC) transmission implementation. At present, there are more than ten HVDC lines in operation in China. By 2020, the power grid of China will become a robust AC-DC system with ultra high voltage links. China will have 33 HVDC lines by then. North China Grid and Central China Grid will become AC-DC hybrid power systems with multi-infeed DC systems. Furthermore, the complexity of East China Grid,

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This paper first analyzes the process and characteristics of big data and cloud computing. A cloud-based architecture for cloud-based power grid wide-area monitoring is proposed. This architecture uses parallel computing and big data mining to provide relevant auxiliary decision-making for the grid. The cloud computing architecture of this paper is based on Hadoop and a brief description of the Hadoop architecture is demonstrated. Finally, two scenarios of grid visualization and grid scheduling decision are briefly described.

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