Abstract
This article presents the control of a three-phase three-wire (3P-3W) dual-stage grid-tied PV-battery storage system using a multi-objective grass-hopper optimization (MOGHO) algorithm. The voltage source converter (VSC) control of the presented system is implemented with adaptive kernel width sixth-order maximum correntropy criteria (AKWSOMCC) and maximum power point tracking (MPPT) control is accomplished using the variable step-size incremental conductance (VSS-InC) technique. The proposed VSC control offers lower mean square error and better accuracy, convergence rate and speed as compared to peer adaptive algorithms, i.e., least mean square (LMS), least mean fourth (LMF), maximum correntropy criteria (MCC), etc. The adaptive Gaussian kernel width is a function of the error signal, which changes to accommodate and filter Gaussian and non-Gaussian noise signals in each iteration. The VSS-InC based MPPT is provided with a MOGHO based modulation factor for better and faster tracking of the maximum power point during changing solar irradiation. Similarly, an optimized gain conventional PI controller regulates the DC bus to improve the power quality, and DC link stability during dynamic conditions. The optimized DC-link generates an accurate loss component of current, which further improves the VSC capability of fundamental load current component extraction. The VSC is designed to perform multi-functional operations, i.e., harmonics elimination, reactive power compensation, load balancing and power balancing at point of common coupling during diverse dynamic conditions. The MOSHO based VSS-InC, and DC bus performance is compared to particle swarm optimization (PSO) and genetic algorithm (GA). The proposed system operates satisfactorily as per IEEE519 standards in the MATLAB simulation environment.
Highlights
The grid-tied Photovoltaic (PV) system has become a natural choice for green energy, considering PV power’s sharp cost curtailment [1]
In the 3P-3W grid-tied dual-stage PV-battery storage system, a nature-inspired metaheuristic technique named multi-objective grass-hopper optimization (MOGHO) algorithm has been implemented for optimization of modulation factor for variable step-size incremental conductance (VSS-incremental conductance (InC)) Maximum power point tracking (MPPT) algorithm and PI controller gains for DC bus regulation
The boost converter efficiency has swollen from 98.82% to 99.82% with optimized VSS-InC MPPT
Summary
The grid-tied Photovoltaic (PV) system has become a natural choice for green energy, considering PV power’s sharp cost curtailment [1]. Adaptive controls have a moderate computational burden and deliver reduced steady-state error with a faster convergence speed. Adaptive algorithms such as least mean square (LMS) [30], least mean fourth (LMF) [31], maximum correntropy criteria (MCC) [32] deliver faster convergence and robustness. The MOGHO algorithm fulfils two objectives by providing the optimum δ for VSS-InC MPPT and PI controller gains (kp, ki) for the Vdc control, in a three-phase three-wire (3P-3W) grid-tied dual-stage PV-battery storage system. MOGHO based scaling factor (δ) optimization for VSS-InC MPPT to achieve faster tracking and reduced power oscillations at MPP. AKWSOMCC based VSC control for fundamental load current component extraction
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