Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems, in which system architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope with current Information and Communication Technologies (ICT). The distributed generation in a smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible, and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review of architectures for smart grids. An in depth analysis of layered and agent-based architectures based on the National Institute of Standards and Technology (NIST) conceptual model is presented. Also presented is a set of smart grid Reference Architectures dealing with cross domain technology.
This paper explains how to predict the severity of the system by connecting with and without a unified power flow controller under different load and n-1 conditions by calculating the LVSI and by the application of data mining and machine learning techniques. A large amount of data will occur during the process of contingency analysis and it is necessary that how to get this to the system value to assess the severity of the system. With the help of data mining and machine learning techniques, analysis of data generated from the simulations for different load conditions are carried out and is used to estimate the severity of the line. In this, the IEEE 30 Bus System is used by calculating the line voltage stability index and the simulation work is carried out by using MATLAB and WEKA software.
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