Power grid equipment data flow cleaning method based on association rules

Abstract

The invention discloses a power grid equipment data flow cleaning method based on an association rule, which comprises the following steps of: calculating association strength of historical data of each data sequence in a data flow by utilizing an Apriorri algorithm, and outputting an association relationship among different data sequences; using an abnormal data screening algorithm based on a sliding time window to detect the data sequences with weak correlation strength one by one; carrying out abnormal data identification processing on the data sequence with higher correlation degree at thesame time; and applying the neural networks with multiple wavelet basis functions to data cleaning to complete combined prediction. The data flow cleaning method is accurate in power grid equipment risk assessment and stable and reliable in data.

Description

technical field [0001] The invention relates to data processing of grid equipment, in particular to a method for cleaning data flow of grid equipment based on association rules. Background technique [0002] At present, online data flow cleaning for power grid equipment in power system has always been one of the important functions of power system state estimation, and the existence of bad data in the data makes it impossible to realize accurate and effective monitoring of power grid equipment. However, in the online data monitoring of power grid equipment, there may be abnormalities in data acquisition equipment, operational errors or blockage of data transmission channels, etc., resulting in bad data with large errors. Because the emergence of bad data caused by the above reasons causes a large deviation in the assessment of the risk of power grid equipment, which in turn increases the complexity and economic cost of power grid maintenance, overhaul and dispatch work, thus...

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Problems solved by technology

[0002] At present, the online data flow cleaning for power grid equipment in power system has always been one of the important functions of power system state estimation, and the existence of bad data in the data makes it impossible to realize accurate and effective monitoring of power grid equipment

However, when performing online data monitoring on power grid equipment, there may be abnormalities in data acquisition equipment, operational errors, or blockage of data transmission channels, resulting in bad data with large errors.

Because the emergence of bad data caused by the above reasons causes a large deviation in the assessment of the risk of power grid equipment, which in turn increases the complexity and economic cost of power grid maintenance, overhaul and dispatch work, thus affecting the safety and stability of the entire power system running

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