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Article

Automatic Identification of Different Types of Consumer Configurations by Using Harmonic Current Measurements

1
Institute of Electrical Power Systems and High Voltage Engineering, Technische Universitaet Dresden, 01062 Dresden, Germany
2
Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: Roberto Langella
Appl. Sci. 2021, 11(8), 3598; https://doi.org/10.3390/app11083598
Received: 8 March 2021 / Revised: 9 April 2021 / Accepted: 12 April 2021 / Published: 16 April 2021
(This article belongs to the Special Issue Power Quality in Electrical Power Systems)
Power quality (PQ) is an increasing concern in the distribution networks of modern industrialized countries. The PQ monitoring activities of distribution system operators (DSO), and consequently the amount of PQ measurement data, continuously increase, and consequently new and automated tools are required for efficient PQ analysis. Time characteristics of PQ parameters (e.g., harmonics) usually show characteristic daily and weekly cycles, mainly caused by the usage behaviour of electric devices. In this paper, methods are proposed for the classification of harmonic emission profiles for typical consumer configurations in public low voltage (LV) networks using a binary decision tree in combination with support vector machines. The performance of the classification was evaluated based on 40 different measurement sites in German public LV grids. This method can support network operators in the identification of consumer configurations and the early detection of fundamental changes in harmonic emission behaviour. This enables, for example, assistance in resolving customer complaints or supporting network planning by managing PQ levels using typical harmonic emission profiles. View Full-Text
Keywords: power system harmonics; harmonic current emission; consumer behavior; public low voltage network; time series; machine learning; classification; support vector machines power system harmonics; harmonic current emission; consumer behavior; public low voltage network; time series; machine learning; classification; support vector machines
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MDPI and ACS Style

Domagk, M.; Gu, I.Y.-H.; Meyer, J.; Schegner, P. Automatic Identification of Different Types of Consumer Configurations by Using Harmonic Current Measurements. Appl. Sci. 2021, 11, 3598. https://doi.org/10.3390/app11083598

AMA Style

Domagk M, Gu IY-H, Meyer J, Schegner P. Automatic Identification of Different Types of Consumer Configurations by Using Harmonic Current Measurements. Applied Sciences. 2021; 11(8):3598. https://doi.org/10.3390/app11083598

Chicago/Turabian Style

Domagk, Max, Irene Y.-H. Gu, Jan Meyer, and Peter Schegner. 2021. "Automatic Identification of Different Types of Consumer Configurations by Using Harmonic Current Measurements" Applied Sciences 11, no. 8: 3598. https://doi.org/10.3390/app11083598

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