Reweighted Sparse- Least-Mean Mixed-Norm Adaptive Control for Solar PV Integrated EV Charging Station

Abstract

Connecting a large number of electric vehicle (EV) to the distribution system, causes the unbalanced loading of the three phases and thereby resulting in the excessive neutral current to flow in the neutral wire of the transformer. To address these issues, in this paper, a solar photovoltaic (PV) array powered multifunctional EV charging station is proposed. The charging station provides the facility to connect multiple single phase EV chargers. Moreover, the charging station employs a voltage source converter (VSC) to interface the PV array and storage battery. The VSC of the charging station performs the multiple tasks such as, (1) extraction of maximum power of the PV array, (2) compensation of neutral current, (3) compensation of harmonics current and reactive power requirement of the EVs, (4) making the three phase source currents balanced and sinusoidal, (5) improvement in the power quality of source voltage and current, and (6) dynamic power management. To achieve all these functionalities, the VSC of the charging station, is controlled using the reweighted sparse least mean mixed norm (RS-LMMN) based adaptive control algorithm. The proposed control algorithm utilizes the benefits of both LMS, and LMF based adaptive control algorithm on minimizing the steady-state error, and improving the convergence rate. Moreover, the proposed control algorithm also adds the correction factor for sparsity in the input matrix. This further improves the tracking capability, convergence rate, and reduces the steady state error. The prototype of the charging station, is used to validate the control algorithm and the various claimed functionalities experimentally.