Optimal state estimation techniques for accurate measurements in internet of things enabled microgrids using deep neural networks

Sudhakar Rao Padupanambur, Mohammed Riyaz Ahmed, Bangalore Prabhakar Divakar


The employment of microgrids in smart cities is not only changing the landscape of power generation, transmission, and distribution but it helps in green alleviation by converting passive consumers into active produces (using renewable energy sources). Real-time monitoring is a crucial factor in the successful adoption of microgrids. Real-time state estimation of a microgrid is possible through internet-of-things (IoT). State estimation can provide the necessary monitoring of grid for many system optimization applications. We will use raw and missing data before we learn from data, the processing must be done. This paper describes various Kalman variants use for preprocessing. In this paper a formulated approach along with algorithms are described for optimal state estimation and forecasting, with weights update using deep neural networks (DNN) is presented to enable accurate measurements at component and system level model analysis in an IoT enabled microgrid. The real load data experiments are carried out on the IEEE 118-bus benchmark system for the power system state estimation and forecasting. This research paves a way for developing a novel DNN based algorithms for a power system under dynamically varying conditions and corresponding time dependencies.


Deep neural network; Internet of things; Kalman filter; Microgrid; Recurrent neural network; Smart grid; State estimation;

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DOI: http://doi.org/10.11591/ijece.v12i4.pp4288-4301

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).