Revamped System Performance of Grid-Interactive Hybrid DFIG-PV System Using WSO Based Dual Layer MRFKMP Adaptive Control

Abstract

Due to the stochastic characteristicsof wind, keeping high-quality power into the grid-dependent hybrid wind energy systems is important for effective energy utilization, especially when there are non-linear loads involved. In this paper, a dual layer random features kernel mean p-power (MRFKMP) adaptive control for the grid interfaced converter of a DFIG-PV based hybrid energy system is implemented for the purpose of maintaining power balances and quality power at both the source and grid ends while operating at unity power factor. The introduced control extracts the weight of stator and load current components with finer convergence and lower computational complexity, excluding complex reference transformations. The dual layer adaptive control improves grid side converter functionality by improving power quality, interruption of loads, taking into account reactive power and harmonic remediation under steady-state and dynamic scenarios.In the secondary step, to achieve optimal DFIG-PV control response, the stability of the DC-link, which is themain coupling element for power transfer between DFIG, solar PV, and battery energy storage (BES), is reinforced by tuning the DC link voltage controller with the white shark optimization (WSO) algorithm. Under the worst-case scenario of large step wind change, the primacy of the envisaged WSO algorithm for tuning DC-link PI controller has been evidenced. It is discovered that WSO tuning positively affects the settling time and oscillatory response of the DC-link voltage not just at the outset, but also throughout the subjugated dynamic wind speedchange.Power flow to the grid is stable due to optimized DC-link voltage performance. WSO parameter tuning improved efficiency is validated using MATLAB simulation results and compared to GA and PSO techniques in this study.