期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2022
卷号：12
期号：4
页码：4288-4301
DOI：10.11591/ijece.v12i4.pp4288-4301
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：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.
关键词：Deep neural network;Internet of things;Kalman filter;Microgrid;Recurrent neural network;Smart grid;State estimation
