Open AccessIoT Based Non–Technical Loss Detection and Mitigation System for Power Distribution Networks
Author(s) -
Emmanuel Eronu,
Matthew O. Oboh,
Emeka S. Ezeh,
Gafar Tiamiyu,
Farouq E. Shaibu
Publication year - 2021
Publication title -
european journal of engineering and technology research
Language(s) - English
Resource type - Journals
ISSN - 2736-576X
DOI - 10.24018/ejeng.2021.6.7.2679
Subject(s) - microcontroller , linear regression , electricity , computer science , power (physics) , regression analysis , work (physics) , electrical engineering , relation (database) , node (physics) , line (geometry) , graph , variable (mathematics) , engineering , data mining , mathematics , mechanical engineering , physics , geometry , structural engineering , theoretical computer science , quantum mechanics , machine learning , mathematical analysis
Electrical Energy crisis is a major problem faced in the world today and it’s increasingly significant in this part of Africa. A perfect solution seems not to be feasible as several solutions have been proposed in the past by various authors with little impact on the power sector. In this work, we present a method of Non-Technical Loss (NTL) detection consisting of a microcontroller interfaced with a current sensor that measures the current on the power line. A sensor node is placed at the supply end of the pole while two or more others sensor nodes are connected to the output of the pole depending on the number of consumers. The measured value of current is sent via the microcontroller to a web cloud that is accessible by the consumers and the utility company from any part of the world by simply logging on to the website; www.electricity-theft.herokuapp.com. The design uses the principle of Kirchhoff Current Law (KCL) to achieve this aim. The consumers can therefore monitor their power consumption from any location in the world and prevent theft on the network. The results obtained from the installation of the sensor nodes were analyzed using correlation and regression analysis. A correlation analysis of the data results gave us a correlation coefficient of 0.9802, while a regression analysis provided us with a linear relationship between the dependent and independent variable expressed mathematically thus Y = 0.916x + 0.254. A regression graph is also plotted. Furthermore, a T-Test and F-Test was conducted to statistically test the sensor nodes. A NodeMCU Wi-Fi microcontroller and a self-powered Phidget current sensor is used for the sensor node design. Communication between the sensor nodes is via Wi-Fi while a 4G router was used to provide internet services.