PQ prediction by way of parallel computing - benchmark and sensitivity analysis for classical ML approaches

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CIRED 2019 - 2049.pdf (689.64 KB)

Paper number

2049

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Conference name

CIRED 2019

Conference date

3-6 June 2019

Conference location

Madrid, Spain

Peer-reviewed

Yes

Short title

Convener

Authors

Eisenmann, Adrian , University of Stuttgart- Institute of Power Transmission and High Voltage Technology, Germany
Streubel, Tim , University of Stuttgart Institute of Power Transmission and High Voltage Technology, Germany
Rudion, Krzysztof, University of Stuttgart, Germany

Abstract

In this paper, three machine-learning (ML) regression methods for the prediction of current and voltage characteristics in industrial environments are investigated. Apart from the original data, three different feature sets are used for the prediction. In addition, up to three different multi-step prediction methods are used and compared - the direct, the recursive and the multi-output method. The regression methods are the k-Nearest Neighbour Regression (kNNR), the Support Vector Regression (SVR), and the Random Forest Regression (RFR). Eleven features are extracted per metric, from which three feature sets are developed by selection based on Kendall rank, Random Forest Feature import (RFFI), and Sequential Random k-Nearest Neighbour (SRKNN). It could be observed that the prediction of voltage is difficult. In this case the prediction error (the mean absolute error (MAE) was used throughout this investigation) could only be improved by the SVR and just by 1.1 % compared to the primitive prognosis. The comparison was based on the assumption that for the primitive prognosis the measured value would remain unchanged over the forecasting period. In predicting the current signal, significantly better results could be achieved, with the SVR again attaining the lowest error with an improvement of 32 %. The least MAE for the voltage prediction was achieved by the feature set selected by RFFI. In the current prediction, the feature selection using SRKNN proved to be best.

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Publisher

AIM

Date

2019-06-03

Published in

CIRED 2019 Conference

Permanent link to this record

https://cired-repository.org/handle/20.500.12455/706
http://dx.doi.org/10.34890/925

ISSN

2032-9644

ISBN

978-2-9602415-0-1