Power electronic converters in the unbalance compensation method for renewable energy-powered active distribution systems: AOA-RERNN approach

Abstract

Unbalanced loads and high neutral currents on low voltage networks which frequently use three-phase, four wire systems with no larger conductors must need to be addressed. To overcome the loads and currents in low-voltage networks, an hybrid method is proposed in this manuscript for improving the networks of low-voltage using three-phase four-wire systems. The AOA-RERNN technique is the integration of the Archimedean-Optimization-Algorithm (AOA) and Recalling-Enhanced-Recurrent-Neural-Network (RERNN) technique to mitigate the issues, like neutral voltage offset, and harmonics, and neutral-to-ground voltage raise. At the point-of-common-coupling (PCC), the integration of Archimedean-optimization-algorithm and Recalling-enhanced-recurrent-neural-network approach is used to overcome the above mentioned issues. This strategy involves optimizing converter parameters with AOA and addressing system imbalances with RERNN, including mid-high line current, phase disparities, and neutral line compensation. Also, implementing control-based compensation reduces neutral current without requiring large neutral conductors. The proposed model is done in MATLAB. By this, the proposed approach achieves an impressive efficiency of 97.54%. But, the existing methods, like Artificial Transgender Long corn Algorithm (ATLA), Combined Adaptive Grasshopper Optimization Algorithm and Artificial Neural Network (AGONN), And Proportional Integral (PI) attain the efficiency of 80.23%, 77.26%, and 82.13%, respectively. The outcome of the simulation indicates that the proposed technique provides better findings than the present methods. Finally, this study demonstrates the possibility of the proposed approach for increasing the efficiency and the performance of electronic power converters in renewable generation.