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An Adaptive ε–Normalized Signed Regressor LMF Algorithm for Power Quality Improvement in Wind-Solar based Distributed Generation System

A wind-solar based distributed generation system (DGS) integrated to the utility grid experiences substantial power quality problems pertaining to uncertain loads at the point of common intersection (PCI) or small under/overvoltage at the grid. This work presents an adaptive ε–Normalized Signed Regressor Least Mean Fourth (ε–NSRLMF) algorithm for mitigating the effects of uncertainty in loading conditions on the DGS. The algorithm significantly reduces the computational burden and thereby simplifies practical implementation of its recursion when compared to a least mean fourth (LMF) and normalized LMF algorithms. A doubly fed induction generator (DFIG) with back-to-back (BTB) connected voltage source converters (VSCs) sharing a common DC link, is utilized for wind generation, owing to its cheap construction and superior performance. A DC/DC boost converter is supplemented at the DC link for solar generation using a solar photovoltaic (SPV) array. The stiff voltage at the DC link even during supply under/overvoltage condition, instigates enhanced high-frequency ripple in the inductor current as well as higher switching losses. Under such conditions, to comply with the permissible total harmonic distortion (THD) limits and to improve system efficiency, this work also presents an adaptable DC link voltage scheme for achieving minimum possible ripple in the grid current. These schemes are corroborated using extensive simulation study and the results demonstrate the power quality improvements in the DGS.