Hybrid Naïve Back Propagation Based iCOSϕ Algorithm for Enriching the Grid Performance with Photovoltaics/Battery/Ultracapacitor/Fuel Cell

Abstract

This paper presents a novel method for the combination of photovoltaic (PV), fuel cell (FC), battery, and ultracapacitor systems with D-STATCOM (Distribution Static Compensator) for grid integration. In this study, a novel naive backpropagation algorithm (NBP) based iCOSϕ is proposed for obtaining fundamental components from load current for effective harmonics compensation and contributes power quality enhancement by delivering power to the grid and connected loads. Further, a modified incremental conductance (MIC) approach is employed to acquire maximum power in the event of different atmospheric conditions. The performance of the proposed method is investigated under four uncertain conditions such as (a) linear loads, (b) zero voltage regulation under dynamic loads, (c) non-linear loads, and (d) variations in solar irradiance. Moreover, the breakthrough of the developed method is compared with various existing techniques like synchronous reference frame (SRF) theory, iCosϕ, fuzzy logic controller (FLC), conductance fryze, adaptive neuro-fuzzy inference system, and gradient descent back propagation learning (GDBP) neural network (NN) based iCosϕ. The simulation outcomes disclosed that the proposed NBP based iCOSϕ technique rendered a prolific performance rather than other existing methods under all uncertain conditions.