Automatic Identification of Different Types of Consumer Configurations by Using Harmonic Current Measurements
Artikel i vetenskaplig tidskrift, 2021

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.

public low voltage network

machine learning

support vector machines

time series

power system harmonics

harmonic current emission

consumer behavior



Max Domagk

Technische Universität Dresden

Irene Yu-Hua Gu

Chalmers, Elektroteknik

Jan Meyer

Technische Universität Dresden

Peter Schegner

Technische Universität Dresden

Applied Sciences (Switzerland)

20763417 (eISSN)

Vol. 11 8 3598





Annan elektroteknik och elektronik



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