A Novel Approach: Effective Compressive Sensing in Power Network Problem

Indrarini Dyah Irawati, Sugondo Hadiyoso, Gelar Budiman


A sparse power matrix is a type of two-dimensional data that conveys the amount of electric power each node, connected to an electric power meter sensor. The need for sensors increases in large power networks. This is problematic due to the increase in the number of sensors resulting in increased costs, the installation is more complicated, attenuation of sensor power, and requires maintenance. In order to reduce the use of sensors and increase the effectiveness of power to the sensors, electrical power measurements can be carried out at certain nodes. This also increases performance due to an efficient communication channel bandwidth and reduces power system data storage. This paper estimates the minimum required sensors on a power grid using compressive sensing (CS). The present study proposes a novel reconstruction algorithm for CS, the effective-OMP (E-OMP), to address this power network problem. E-OMP determines the minimum number of sensors in use. The study simulations reveal that E-OMP can save power from sensor use and preserve storage space. The proposed reconstruction algorithm generates NMSE less than 0.05 and has guaranteed high reconstruction accuracy compared to other ones (OMP, IRLS, and L1 reconstruction algorithms.



Keywords: compressive sensing, effective-OMP, power network, reconstruction.

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