Cost-effective telemetry for energy network of an electricity distribution company: part I

Author:

UD DIN ASIM ZAHEERORCID,AYAZ YASARORCID,AHMAD MIAN ILYAS AHMADORCID,MASAUD SALMANORCID,HASAN MOMENAORCID,MUHAMMAD NAVEEDORCID

Abstract

We present a novel application of radio frequency wireless mesh network and general packet radio service technologies in a telemetry solution to measure power flow in the energy network of an electricity distribution company. The telemetry solution utilizes some selected circuits of grid stations and calculates total power consumed, total power imported, and total power exported by the distribution company. The selection of circuits for sensors installation is the key for reducing solution cost as compared to the case when sensors are installed on all the power output points. The framework involves installation of specially developed energy sensors (smart energy meters) and data concentrator units at the selected grid stations for measurement of energy data that include active energy, reactive energy, active power, apparent power, current, voltage, and power factor. The measured data reach the data concentrator unit using a 433-MHz wireless mesh network and are transmitted to a remote power control center using general packet radio service. Energy data from different grid stations across the energy network are collected at the power control center and utilized in calculation of total power consumed, total power imported, and total power exported. The approach has been tested on two electricity distribution companies of Pakistan: the Islamabad Electric Supply Company and Peshawar Electric Supply Company. Also in this work, the result of overload detection based on a generalized likelihood ratio test for an industrial feeder of the Islamabad Electric Supply Company is included. Detection probability of 0.96 with a false alarm probability of 0.04 has been achieved for a 30-min data interval.

Publisher

The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM)

Subject

Electrical and Electronic Engineering,General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Bivariate Short-term Electric Power Forecasting using LSTM Network;2019 International Conference on Robotics and Automation in Industry (ICRAI);2019-10

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