Abstract
Renewable energy resources connected to a single utility grid system require highly nonlinear control algorithms to maintain efficient operation concerning power output and stability under varying operating conditions. This research work presents a comparative analysis of different adaptive Feedback Linearization (FBL) embedded Full Recurrent Adaptive NeuroFuzzy (FRANF) control schemes for maximum power point tracking (MPPT) of PV subsystem tied to a smart microgrid hybrid power system (SMG-HPS). The proposed schemes are differentiated based on structure and mathematical functions used in FRANF embedded in the FBL model. The comparative analysis is carried out based on efficiency and performance indexes obtained using the power error between the reference and the tracked power for three cases; a) step change in solar irradiation and temperature, b) partial shading condition (PSC), and c) daily field data. The proposed schemes offer enhanced convergence compared to existing techniques in terms of complexity and stability. The overall performance of all the proposed schemes is evaluated by a spider chart of multivariate comparable parameters. Adaptive PID is used for the comparison of results produced by proposed control schemes. The performance of Mexican hat wavelet-based FRANF embedded FBL is superior to the other proposed schemes as well as to aPID based MPPT scheme. However, all proposed schemes produce better results as compared to conventional MPPT control in all cases. Matlab/Simulink is used to carry out the simulations.
Highlights
The energy demand of the globe is mainly fulfilled by fossil fuel
This paper presents a comparison of the performance of four different adaptive feedback linearization (FBL) techniques incorporated with full recurrent adaptive NeuroFuzzy (FRANF) based controllers for a PV system in a grid-integrated SMG-HPS for three different cases
Three different cases are taken in this research work, e.g., (a) Step change in both solar irradiation and temperature; (b) Partial shading condition; and (c) Daily field data of solar irradiation and temperature in Islamabad, to evaluate the performance of proposed adaptive Feedback Linearization (FBL) embedded Full Recurrent Adaptive NeuroFuzzy (FRANF) controllers under partial shading conditions (PSC) compared to adaptive PID (aPID) control scheme
Summary
The energy demand of the globe is mainly fulfilled by fossil fuel. Increasing energy demand and limitation of fossil fuel supplies boost the cost of electricity. Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control and efficiently needs its conversion into electrical energy [3]. The NeuroFuzzy algorithm combines the reasoning method of humans in fuzzy systems with the learning abilities of neural networks This hybrid adaptive scheme can deal with system nonlinearities, uncertainties, and fluctuations. Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control conventional and non-conventional techniques were developed to tackle PSC in the near past but they have drawbacks of lager power fluctuation, lower power output, and complexity of control design in some cases [32,33,34,35,36,37]. This paper presents a comparison of the performance of four different adaptive feedback linearization (FBL) techniques incorporated with full recurrent adaptive NeuroFuzzy (FRANF) based controllers for a PV system in a grid-integrated SMG-HPS for three different cases.
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