| Summary: | The wide-ranging use of renewable energy sources (RES) in the modern era leads to the association of distributed non-conventional energy resources in contrary to large-scale and localized conventional non-renewable sources. The prevalent use of distributed sources produces various Power-Quality issues. A thorough review of the literature provides numerous approaches for Power Quality (PQ) improvements; however, most techniques can improve only a few PQ problems. The new technical challenges like power loss, stability, reliability and power quality (PQ) issues are observed in present micro grid scenario. In this regard, this research work is an attempt to address PQ improvement in a three-phase realistic complex micro grid (integration of three simple micro grids to the point of common coupling. Improvement of various PQ factors for example sag/swell, unbalancing, power factor (Pf), total harmonic distortion (THD), communication delay and addition of impedance has been addressed in this research work. In this research article, a Kernel based Deep Auto Regressive Exogenous Output Neural Network (KDNARX) controller has been proposed and applied to improve power quality issues in a complex or realistic three phase complex micro grid (RTPCMG) in grid connected mode of operation. The RTPCMG is a combination of three simple grids with individual ratings of (4 kW, 4 kW and 6 kW). Photo voltaic cell, wind generator (WG), fuel cell (FC) and battery energy storage system (BESS) are the constituent distributed generators (DGs) for the proposed micro grid. The PQ improvement by application of proposed KDNARX controller to the DG inverter switching, has been overly tested in 12 different power quality issues under various operating conditions. The Kernel parameters are optimized by a new optimization technique called Cosine Chaotic PSO (CCPSO) algorithm. The results proved the strength of the new control technique ensuring PQ improvement and stability study of a three phase AC micro grid. Also, the potentiality of the new control scheme is compared with ANN and NARX and its superiority has been proved. Further, some case studies are validated through hardware in loop (HIL) environment thus justifying its real time implementation. © 2023 The Author(s)
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