Hybrid renewable energy based smart grid system for reactive power management and voltage profile enhancement using artificial neural network

Abstract

ABSTRACT This article presents the design of an advanced smart grid model with an improved optimal variation strategy by implementing renewable energy sources. The novelty involved in this article is designing a renewable energy-based smart grid system incorporating with Artificial Neural Network for maintaining an improved voltage profile with balanced reactive power level across the grid network. The objective is attained by implementing renewable energy sources, namely solar and wind energy modules, with artificial intelligence techniques for effective reactive power control using DSTATCOM. The power requirement across the power grid network is satisfied by providing solar and wind energy sources managing the smart-grid system’s var level. The optimized power is trained on the Feed Forward Neural Network-based for calculating the amount of voltage profile and power loss based on system load. The DSTATCOM is an optimal var compensating device for maintaining the reactive power within the permissible limit across the smart grid system. The proposed system improves the voltage profile, reduces the power loss, and satisfies the grid by managing the system’s reactive power balance. The proposed approach is executed in MATLAB/Simulink work site, and the performance is analyzed with three different cases. The smart grid system powered with hybrid energy sources strives to attain the objective functions effectively among the three different cases. Integration of hybrid renewable energy source with Artificial Neural Network achieves an improved voltage profile to about 98.45%, which states that the profile rating has improved from 1.056 to 1.083 p.u minimizations of real power loss has done from to 3.23 MW from 3.80 MW.