Effective Data Compression Model for On-Line Power System Applications

Abstract

The main objective of this paper is to develop an efficient data compression model for online power system applications such as load flow studies, state estimation, contingency analysis etc and to calculate the round trip-time taken for sending the compressed data in client/ server architecture. Martin Burtscher algorithm is used for data compression since most of the power system data is expressed in per unit representation which is in floating point format. Many research works have been reported for representing and solving power system problems in distributed environments which include RMI, Component based, SOA and Grid computing. As the size of the power system is growing large and large due to increase in demand and the interconnections between large power systems may vary time to time due to addition of new generating units and due to geographic conditions, it becomes difficult to estimate the current operating states of the real time electric power system network and data communications between the network becomes difficult. The proposed method of power system data compression finds faster rate of data communications where the data is required for realtime analysis in a distributed environment. 

Effect of line length on the fault current due to line to ground fault in transmission line using MATLAB Simulink

“…The basic purpose of power system protection is to meet this requirement. There are numerous ways to find and identify power system issues[3]- [5].…”

Enhanced R package-based cluster analysis fault identification models for three phase power system network

“…There were a great deal of information mining analysis required in DMT, for example, classification, evolution analysis, generalisation, association analysis, outlier analysis and cluster analysis. Lot of research work has been carried out in the field of computer applications to power system engineering (Nithiyananthan and Jain, 2015;Mani et al, 2015;Hau et al, 2015;Pavithern et al, 2015;Nithiyananthan and Nithya, 2015;Nithiyananthan and Ramachandran, 2014, 2013a, 2013b, 2010, 2008a, 2008b, 2005a, 2005b, 2004a, 2004b, 2003a, 2003b, 2003c, 2002Nithiyananthan and Loomba, 2011;Nithiyananthan and Elavenil, 2011;Nithiyananthan and Jacob, 2011;Nithiyananthan, 2009, 2008;Nithiyananthan et al, 2006). As expressed above there was a great deal of DMT utilisation in different fields.…”