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Statistical Approach in Analyzing of Advanced Metering Data in Power Distribution Grid
Author(s) -
Ivan Ramljak,
Drago Bago
Publication year - 2019
Publication title -
journal of communications software and systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.191
H-Index - 13
eISSN - 1846-6079
pISSN - 1845-6421
DOI - 10.24138/jcomss.v15i2.683
Subject(s) - metering mode , smart grid , computer science , smart meter , electricity , grid , automatic meter reading , transformer , electric power distribution , distribution transformer , electricity meter , energy consumption , electrical engineering , voltage , real time computing , automotive engineering , power (physics) , telecommunications , engineering , mechanical engineering , physics , geometry , mathematics , quantum mechanics , wireless
In last period many distribution system operators (DSO) invest significant amount of money in smart metering system. Those investments are in part due to regulatory obligations and in part due to needs of DSO (utilities) for knowledge about electric energy consumption. Term electric energy consumption refers not only on real consumption of electric energy but also on data about peak power, unbalance, voltage profiles, power losses etc. Data which DSO can have depends on type of smart metering system. Further, smart meters as source of data can be implemented in transformer stations (TS) MV/LV and in LV grid at consumer level. Generally, smart meters can be placed in any node of distribution grid. As amount of smart meters is greater, the possibility of data analysis is greater. In this paper a smart metering system of J.P Elektroprivreda HZ HB d.d, Mostar, Bosnia and Herzegovina will be presented. One statistical approach for analyzing of advanced metering data of TS MV/LV will be presented. Statistical approach presented here is powerful tool for analyzing great amount of data from distribution grid in simple way. Main contribution of this paper is in using results obtained from statistical analysis of smart meter data in distribution grid analyzing and in maintenance/investment planning.

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