Automatic Identification of Different Types of Consumer Configurations by Using Harmonic Current Measurements
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
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.
Details
Original language | English |
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Pages (from-to) | 3598 |
Number of pages | 1 |
Journal | Applied sciences |
Volume | 11 |
Issue number | 8 |
Publication status | Published - 1 Jan 2021 |
Peer-reviewed | Yes |
External IDs
Scopus | 85104983968 |
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ORCID | /0000-0001-5951-2033/work/142241877 |
ORCID | /0000-0001-8439-7786/work/142244165 |
Keywords
Sustainable Development Goals
Keywords
- classification, time series, harmonic current emission, power system harmonics, support vector machines, consumer behavior, machine learning, public low voltage network