Method for identifying and repairing power load abnormal data based on density clustering and LSTM

Abstract

The invention discloses a method for identifying and repairing power load abnormal data based on density clustering and LSTM, and belongs to the technical field of power quality analysis methods. According to the method, a density-based clustering algorithm (Density-based Spatial Clustering of Applications width Noise) and Long Short-Term Memory Neural Network are combined to identify and repair abnormal data. The method comprises the following steps: firstly, carrying out density clustering on data in units of days by utilizing a DSCAN algorithm to obtain abnormal data; then, using a long short-term memory (LSTM) neural network, taking the time series data judged to be abnormal as input of the LSTM neural network, and using the first n pieces of sequence data to predict the next piece ofsequence data; finally, the predicted value of the LSTM serving as an accurate value, setting an up-down floating threshold value is set, if the measured value exceeds the threshold value range, regarding the measured value as an abnormal value, and the predicted value of the LSTM serving as a correction value. According to the method, the time sequence and regularity of the power quality monitoring system data in the actual power grid are fully considered, the specific abnormal value can be accurately detected and repaired, and the method has good actual application value.

Description

technical field [0001] A method for identifying and repairing abnormal power load data based on density clustering and LSTM belongs to the technical field of power quality analysis methods. Background technique [0002] With the rapid development of my country's social economy and the significant improvement of residents' living standards, the amount of load data in the smart grid is increasing. However, due to factors such as transformers and other hardware equipment, smart grid test environment, parameter configuration and manual recording errors, there will always be some abnormal data. These abnormal data interfere with data analysis, and may even bring wrong analysis results. In order to ensure the accuracy of later analysis, it is necessary to identify and repair abnormal data of the original data before data analysis. [0003] For the first time, this method uses a combination of density-based clustering algorithm and long-term short-term memory neural network (LSTM...

AI Technical Summary

Problems solved by technology

However, due to factors such as transformers and other hardware equipment, smart grid test environment, parameter configuration and manual recording errors, there will always be some abnormal data.

These abnormal data interfere with data analysis, and may even bring wrong analysis results

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