ALSO-DCGNN: enhancing power quality and voltage regulation of energy storage systems in DC micro grid

The need to maintain demand and enhance power quality in Renewable Energy Resource (RER) requires significant reliance on energy storage systems. This paper proposes a hybrid technique for enhancing power quality and voltage regulation of energy storage systems in DC Micro Grid (MG). The proposed hybrid approach is a combination of both Artificial Lizard Search Optimization Algorithm (ALSO) and Density Clustering and Graph Neural Network (DCGNN). Hence, it is named as ALSO-DCGNN technique. The primary goal of the proposed approach is to minimize total harmonic distortion (THD), and regulates DC bus voltage to improve the power quality of DCMG. The ALSO algorithm is used to control inverter of the DC micro grids (DCMG) system and DCGNN is used to predict parameters. The proposed method is implemented in MATLAB and compared with existing approaches. The existing methods like the Seagull Optimization Algorithm, grasshopper optimization Whale Optimization Algorithm. The proposed approach ALSO-DCGNN reduces the THD by 2% and the settling time to 1.02 s it is lower, sensitivity is 98.5% is high and efficiency of 97% higher than the existing approaches. By minimizing THD, ALSO-DCGNN helps mitigate potential issues such as equipment damage, interference with communication systems, and decreased efficiency in power distribution networks.

Graphical abstract of grid independent DC micro-grid.