Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control schemes for photovoltaic system in microgrid

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 Neuro-Fuzzy (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.

Legendre-wavelet embedded NeuroFuzzy feedback linearization based control scheme for PHEVs charging station in a microgrid

“…The power rating of all the sources is given in Table 1 [22]. Other details of SMG-HPS control, solar profile, etc., are given in [22]. aPID is used to control all source converters except CS.…”

“…Table 2 gives the detail of PHEV's batteries [3,24]. FBL is a technique that converts the nonlinear dynamics of the system into linear dynamics algebraically, partly or fully, thus linear control scheme can be applied easily [22].…”

“…For the best approximation of f (x) and g(x) in ( 8), a seven layer FRANF-Leg-WC is used in this research work as shown in Figure 3 [22]. The inputs to the FRANF-Leg-WC is the difference between the output voltage of PHEVs voltage regulator and AC-bus voltage.…”

“…However, FL has problems with exponential growth in the number of membership functions and if-then rules. It needs to be re-defined whenever the system parameters change, whereas ANN requires periodic training for its convergence [15,22].…”

MPPT Algorithm Based on Artificial Bee Colony for PV System

“…T HE high use of generators, loads and storage systems to the main grid, turns the energy transformation more challenging, and so control objectives are demanded according to the needs of modern electrical systems [1], [2]. This is the case, for instance, of grid-connected systems such as renewable energy [3]- [5].…”