Fault location method for distribution networks using smart meters. (May 2017)

• Record Type: Journal Article
• Authors: Jamali, Sadegh; Bahmanyar, Alireza; Bompard, Ettore
• Is Part Of: Measurement. Volume 102(2017)
• Page Start: 150
• Page End: 157
• Publication Date: 2017-05
• Subjects: Bad data identification -- Distribution networks -- Fault location -- Smart meters -- State estimation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.02.008
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The proposed fault location method is simple and does not require the fault type. The method is not affected by different fault types and fault resistances. The method considerably reduces the impact of measurement and load data errors. The method can be implemented using a limited number of meters. Abstract: The ongoing deployment of smart meters, with data processing and communication features, has provided the opportunity to improve distribution systems performance. This paper presents a state estimation-based method for fault location in distribution networks using the measurements provided by the smart meters. During the normal operation of the system, state estimation methods can handle the errors in real or pseudo-measurements to give the best estimate of the system state and to identify large measurement errors, called bad data. This concept is extended in this paper under fault conditions where the fault is considered as an unknown and temporarily connected load which can be dealt with as bad data. The proposed method uses the changeable weighting matrix bad data identification technique to find the fault location. The method is algorithmically simple and does not require the fault type. The fault location accuracy is verified by extensive simulation tests on a real 13.8 kV, 134-node distribution network for different fault scenarios. The results indicate that the method has a good performance under measurement and load data errors and for different number and locations of the smart meters in the network.