Filling of missing values of voltage data based on ResIOFNN

Abstract

Abstract With the increasing scale of power Systems, the amount of data that needs to be collected increases exponentially, and data collection equipment will inevitably experience different degrees of data loss. Traditional missing data filling algorithms, such as Expectation Maximization Algorithm (EM) and K Nearest Neighbors (KNN), have low accuracy when dealing with missing data. Given the limitations of the current time series data-filling model, this paper combines the idea of deep learning technology. It proposes an improved voltage missing value-filling algorithm based on the Fourier neural network model. The model can combine the future and past information of the missing data to complete the filling work on the missing data set, which improves the precision of voltage data filling. The calculation example adopts the data of the real power grid for simulation analysis, and the calculation outcomes prove the data-filling method’s high level of fill accuracy.