Advanced metering infrastructure for low voltage distribution system in smart grid based monitoring applications

Abstract

Smart grid system is an emerging technology used for solving the issues associated with power and energy. Smart grid system is known to be an automatic network, ensuring power with the transfer of information and electricity between the power plants and household appliances. However, there is a lack in the data flow about the voltage distribution stability, accuracy and applicability. So, the advanced metering infrastructure, personal energy management and Information Technology are overviewed in this model. The features of smart meter enable the advanced metering infrastructure monitoring and investigates low-voltage (LV) network. This is achieved by optimal state, in which the power quality and outage data is transferred by the distribution network from smart meters to the control center. In this model, the smart grid architecture is mapped with optimal states to observe the LV network processing. Recursive Karhunen–Loéve Transform Grid state optimization Model (RK-LTGM) is proposed in this paper for ensuring the usage of new data and narrow down the variations in estimator range. The proposed model is robust against the network loss. Dimension Reduction Estimation Model Probabilistic Loss (DREM-PL), Korhonen Estimation Model (KEM) and Constant Network Loss Rate Estimation Model (CNLREM) are the three state-of-art methods considered for comparing this model. Recursion Least Squares (RLS) and Damped Recursion Least Squares (DRLS) are the index parameters considered in the proposed method. The proposed Recursive Karhunen–Loéve Transform Grid state optimization Model (RK-LTGM) helps in exchanging information in multilevel smart grid, allowing Smart meter to utilize the information to obtain the high quality best technological and economical solution for every consumer. It is found that the proposed RK-LTGM achieves 0.112 % of RMSE, 0.156 % of network loss rate, 56.124 % of success rate under RLS condition, 0.067 % of RMSE, 0.137 % of network loss rate and 88.739 % of success rate under DRLS condition.