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.
Highlights
The results showed that the harmonic emission of different consumer configurations is strongly related to the typical usage of the connected equipment
The analysis of the 40 measurements sites within the German measurement campaign showed that characteristic emission profiles, especially of the 3rd harmonic current are most suitable for representing different consumer configurations
Emission profiles should be manually reviewed by the user of the classification system
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. One of the reasons is the integration of new device technologies on a large scale, like photovoltaic inverters, the transition from incandescent to LED lamps, and the integration of battery electric vehicles and smart distribution systems. This may result in an increased deterioration of PQ, which could in return affect other electronic devices, in particular if their sensitivity to a particular PQ phenomenon (e.g., harmonic distortion) is high (low immunity). The integration of intelligent electronic devices (IED), like energy meters, enables extremely cost-efficient solutions [4] Due to this trend, network operators have intensified their PQ measurement activities and the number of monitored sites rapidly increases. The last section introduces a measure of misclassification, which allows the user of the classification system to identify emission profiles that are incorrectly assigned to the defined classes of emission profiles
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
