Abstract
This paper presents Nyström minimum kernel risk-sensitive loss (NysMKRSL) based control of a three-phase four-wire grid-tied dual-stage PV-hybrid energy storage system, under varying conditions such as irradiation variation, unbalanced load, and abnormal grid voltage. The Voltage Source Converter (VSC) control enables the system to perform multifunctional operations such as reactive power compensation, load balancing, power balancing, and harmonics elimination while maintaining Unity Power Factor (UPF). The proposed VSC control delivers more accurate weights with fewer oscillations, hence reducing overall losses and providing better stability to the system. The seamless control with the Hybrid Energy Storage System (HESS) facilitates the system’s grid-tied and isolated operation. The HESS includes the battery, fuel cell, and ultra-capacitor to accomplish the peak shaving, managing the disturbances of sudden and prolonged nature occurring due to load unbalancing and abnormal grid voltage. The DC link voltage is regulated by tuning the PI controller gains utilizing the Salp Swarm Optimization (SSO) algorithm to stabilize the system with minimum deviation from the reference voltage, during various simulated dynamic conditions. The optimized DC bus control generates the accurate loss component of current, which further enhances the performance of the proposed VSC control. The presented system was simulated in the MATLAB 2016a environment and performed satisfactorily as per IEEE 519 standards.
Highlights
Grid integrated photovoltaic (PV) systems are gaining substantial significance in the modern grid scenario
The variable parameter zero-attracting least mean square (LMS) (VPZALMS) [13] uses an adaptive coefficient to smooth out the transients, which results in better performance
The proposed control based on the Nyström minimum kernel risk-sensitive loss (NysMKRSL) [15] algorithm with a linear growth network can manage both Gaussian and non-Gaussian noises with high filtering accuracy and robustness, as compared to other adaptive control algorithms
Summary
Grid integrated photovoltaic (PV) systems are gaining substantial significance in the modern grid scenario. The voltage source converter (VSC) controls based on conventional, adaptive, predictive, and artificial intelligence (AI) techniques have been implemented on a grid-tied PV system. A. VDC regulation: The DC link voltage is regulated with the gain optimized PI controller based on the SSO algorithm that generates an accurate loss component of current and enhances the stability of the system. Based maximum power point tracking (MPPT) control, NysMKRSL-based VSC control, seamless control for islanding and re-synchronization, bi-directional converter control for battery and UC charge control, and VDC control by conventional PI and PI gains optimization by the SSO algorithm. The research methodology utilized in the presented work is shown, where PV voltage, current (VPV, IPV), DC bus voltage (VDC), battery, and UC current (IBAT, IUC) are sensed for controlling the SSO-based VDC, optimization boost converter, and battery and UC current. Three steps to check the mechanisms for islanding detection, grid re-synchronization detection, and grid re-synchronization confirmation are provided for effortless and seamless transition of VSC controls
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