A fuzzy inference system for disturbance classification in power distribution networks using a low-cost PMU

Abstract

Electrical distribution networks are evolving at a high pace, mainly due to the increase of distributed energy sources, the growing use of advanced metering infrastructures and new management systems. Phasor Measurement Units (PMUs) have the potential to play an essential role in the monitoring of power distribution systems. This paper proposes a fuzzy inference system to be embedded in a low-cost PMU for classifying the origin/cause of electrical disturbances in power distribution systems. Precise knowledge of the origin of electrical disturbances is fundamental to meet the requirements of load and network modeling tasks. Experimental results show the effective operation of the fuzzy inference system and its ability to address the measurement inaccuracies of the PMU. Furthermore, a remote server was used to correctly estimate electrical parameters of loads by using the output information of the fuzzy inference system.