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Architecture of the Internet of Energy Network: An Application to Smart Grid Communications
Author(s) -
Masud Rana
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2683503
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Due to the global warming and energy crisis, the renewable distributed energy resources, such as wind turbines, are integrated into the grid. We model an AC microgrid with energy generating units, local loads, and electronic devices. Then, the set of non-linear differential equations are expressed as a state-space model. As the microgrid is located in the customer premises or remote areas, its condition needs to monitor in real-time. So, the smart sensor requires to deploy around the microgrid, and its sensing information transmits to the energy management system via the Internet as the sensing information is a massive amount of data. Combining the Internet of Things elements, such as sensors (Internet emended), and the Internet as a transmission medium will form the Internet of Energy, which is considered as a sign interest nowadays. Basically, the energy management center estimates the microgrid states to know the operating conditions of these foreseeable intermittent resources. For estimating the microgrid states, the H-infinity-based Mimi-max filter is proposed, which will no need to know the exact process and measurement noise statistics. Simulation results show that the proposed approach can well estimate the system states compared with the existing Kalman filter. As a result, this framework will assist to design a suitable microgrid framework and provides effective dynamic state estimations.

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