Abstract

Smart energy meters supporting bidirectional data communication enable novel remote error monitoring applications. This research targets characterization of the systematic worst-case error of the previously published remote watthour meter’s gain estimation method based on the comparison of synchronous measurements by the reference and meter under test. To achieve the research aim a methodology based on global maximization of the systematic error objective function assuming the typical low voltage electrical distribution network operation parameters ranges as defined by the standard recommendations for network design. To cross verify the reliability of the assessed solutions the suggested error analysis methodology was implemented utilizing two stochastic global extremum search techniques (genetic algorithms, pattern search) and the third one utilizing nonlinear programming solver. It was determined that the wattmeter adjustment gain worst-case error does not exceed 0.5% if the remote wattmeter monitored load power factor is larger than 0.1 and a network is designed according to the recommendation of the acceptable voltage drop less than 5%. For a load exhibiting power factor larger than cos φ = 0.9 the worst-case error was found to be less than 0.1%. It is concluded therefore that considering the systematic worst-case error the previously suggested remote wattmeter adjustment gain estimation method is suitable for remote error monitoring of Class 2 and Class 1 wattmeters.

Highlights

  • IntroductionThe increasing amount of distributed electrical energy generation, smart revenue and subaccounting meters, interconnected via the advanced measurement infrastructure (bidirectional data communication channels between meters and data collection modules) are the key features of the evolving modern smart electrical grids

  • The increasing amount of distributed electrical energy generation, smart revenue and subaccounting meters, interconnected via the advanced measurement infrastructure are the key features of the evolving modern smart electrical grids

  • The results presented in this chapter are aimed to revealδk pdependence on Pl, cos φ and Pw {Pl

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Summary

Introduction

The increasing amount of distributed electrical energy generation, smart revenue and subaccounting meters, interconnected via the advanced measurement infrastructure (bidirectional data communication channels between meters and data collection modules) are the key features of the evolving modern smart electrical grids. The demand the metrological reliability of the readings (trusted metering) of measurement devices cannot be underestimated. It is crucial for maintaining the correct energy consumption billing (revenue metering) and provides the electricity consumer with their energy consumption budget (submetering, home automation, etc.) which on its way enables changing the energy consumption behavior towards better energy efficiency of buildings, logistics, manufacturing processes, etc. The infrastructure last decade isofmarked withthe a energy significant shift towards replacing themarked electrowith a significant shift towards replacing the electro-mechanical meters with smart electronic meters mechanical meters with smart electronic meters featuring various communication capabilities. Featuring various communication the sub-accounting meters, automation the sub-accounting meters, homecapabilities

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