Power distribution network load calibration method and power distribution network load calibration device based on big data analysis

Abstract

The invention discloses a power distribution network load calibration method based on big data analysis. The method comprises the steps of acquiring basic data based on a power distribution automatic system; filtering distortion load curves which are affected by a specific event through associated data analysis; eliminating load curved with severe shape distortion by means of an average per-unit value method; processing burr data according to a 3sigma principle; and completing lost data through an interpolation method. The invention further provides a power distribution network load calibration device based on big data analysis, wherein the power distribution network load calibration device comprises a data acquiring module and a data calibration module. According to the power distribution network load calibration method and the power distribution network load calibration device, big data analysis technology is utilized; intelligent identification and abnormity correction are performed on power distribution network load data in multiple dimensions, thereby ensuring high effectiveness of the load data and improving quality and efficiency in load calibration. Furthermore the power distribution network load calibration method is concise and effective and is suitable for engineering practical application.

Description

technical field [0001] The invention relates to the technical field of distribution network load calibration, in particular to a method and device for distribution network load calibration based on big data analysis. Background technique [0002] The load data in power grid operation has many important uses, such as load forecasting, openable capacity management, operation mode optimization, economic operation analysis, planning and design, etc. At present, there are weak links in the grid structure of the distribution network in various regions, and the quality of the load data is poor. The calibration of the load data of the distribution network is generally carried out by multi-dimensional system analysis. The data is not comprehensive, and the workload of personnel is heavy. Inefficient and less effective. [0003] Based on the distribution network automation system, it is of great significance to develop an efficient and easy-to-use automatic load calibration method by...

AI Technical Summary

Problems solved by technology

The data used in the calculation is inaccurate, or relies on manual verification; load changes caused by the adjustment of the distribution network operation mode are not considered, etc.

Generally speaking, distribution network load calibration generally uses limited historical load data of distribution network under manual intervention, and in some cases even uses static data such as current limit values ​​for rough estimation, which is difficult to reflect the dynamic changes of power grid operation, and the calculation results belong to Rough calculation, low accuracy; heavy workload, low efficiency and error-prone

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