Smart sensor network for power quality monitoring in electrical installations. (June 2017)

• Record Type: Journal Article
• Authors: Morales-Velazquez, Luis; Romero-Troncoso, Rene de Jesus; Herrera-Ruiz, Gilberto; Morinigo-Sotelo, Daniel; Osornio-Rios, Roque Alfredo
• Is Part Of: Measurement. Volume 103(2017)
• Page Start: 133
• Page End: 142
• Publication Date: 2017-06
• Subjects: Smart sensor network -- Power quality -- FPGA -- Smart grid; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.02.032
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: As smart sensor network is proposed in order to monitor an electrical installation. The developed system is able to detect power quality events and correlate them. Three study cases are presented: residential, industrial, and hospital facilities. Proposed smart sensors is able to calculate real-time indexes and big-data logging. Abstract: Smart meters are one of the basic components of the future smart grid, they allow remotely monitoring each point in the grid in order to know in real-time the performance of the system and to detect potential failures. In this paper, a smart sensor network is introduced and the most important features are presented in three different scenarios: a residential home, an industrial installation, and a public building. The proposed system demonstrates its capabilities of in situ real-time processing and big-data off-line network processing. The suggested smart meter is based on field programmable gate array (FPGA) technology that allows a reconfigurable architecture, which lets the user to select the proper processing modules according to their application. The developed smart sensor network calculates standard figures such as effective values, power factor, and total harmonic distortion; in addition, it detects power quality disturbances such as dips, swells, or interruptions. Moreover, the smart sensor network can continuously detect events to identify certain kind of appliances or industrial equipment such as: fans, lighting, microwave ovens, refrigerators, among others; it is a powerful tool to analyze an entire building in a non-intrusive load monitoring approach.