Method and system for cleaning mass data of energy storage power station

Abstract

The invention provides a method for cleaning mass data of an energy storage power station and a system for cleaning mass data of the energy storage power station. The method for cleaning mass data of the energy storage power station comprises the following steps: I, positioning and replacing default value of data concentration of the energy storage power station; II, positioning and replacing abnormal value of data concentration; III, determining unreasonable data from the data concentration obtained after replacing according to different types and characteristics of the energy storage battery data, and replacing it. The method and the system can clean the mass battery data, guarantee the mass data distribution processing requirement, realize the mass battery data optimization cleaning and pretreatment purposes of the energy storage power station after comprehensively considering about K neighbor algorithm, Pauta criterion method, distributed processing, and others; besides, the pretreatment and using effects of the mass data of the large-capacity battery energy storage power station are improved.

Description

technical field [0001] The present invention relates to a method and system in the technical field of energy storage, in particular to a method and system for cleaning massive data of an energy storage power station. Background technique [0002] At present, the data collection, storage and management methods of energy storage power stations are not yet standardized, and further research on massive data management and mining technologies for energy storage power stations is needed. The massive data of the energy storage power station mainly has the following characteristics: (1) Large amount of data: due to the large number of batteries in the energy storage power station, each battery has a lot of monitoring equipment, and the amount of data collected per second is huge, so it is required to be able to clean it correctly and quickly These data. (2) The reasons for abnormal data are complicated: due to the large number of monitoring equipment, which are affected by various ...

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Problems solved by technology

(2) The reasons for abnormal data are complex: due to the large number of monitoring equipment, which are affected by various objective and unpredictable factors such as accuracy and network signals, there are abnormal data in the data

[0005] 1. The range of single-batch processing is limited, resulting in a small number of mathematical statistical analysis each time and low cleaning accuracy;

[0006] 2. It cannot cope with the parallel processing of massive data, and single-line cleaning takes a long time, is slow, and is not efficient

[0007] 3. There are many types of data, and various situations need to be considered in a single batch, and the processing is more complicated, which increases the difficulty of calculation

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