A Method of Estimating Transmission Line Parameters Using Cloud Computing Based on Distributed Intelligence

The popularization and application of PMU measurement devices in power system provides real-time data monitoring tools for power grid operators. However, the measuring time interval of measuring devices is extremely short. The processing and analysis of the big data generated by measuring devices presents new requirements for the power system, and brings new challenges to the operators. In this paper, the method of parameter estimation of transmission line using cloud computing based on distributed intelligence is studied in depth. An efficient solution aim at processing the big data is given. The k-means clustering algorithm is used to fit the actual situation of the transmission line parameters under the temperature and humidity micro-meteorology. A new way of the mass data application is provided in this paper. The experimental example proves that the cloud computing model based on distributed intelligence can greatly improve the computational efficiency and save the computing time. In addition, the parameters of the transmission line in micro-meteorology conform to the actual operation of the power grid, and early warning can be provided to operators when the real-time operating parameters change suddenly.