Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied pv system using fuzzy controller

Abstract

The photovoltaic (PV) generator exhibits a nonlinear -characteristic and its maximum power (MP) point varies with solar insolation. In this paper, a feedforward MP-point tracking scheme is developed for the coupled-inductor interleaved-boost-converter-fed PV system using a fuzzy controller. The proposed converter has lower switch current stress and improved efficiency over the noncoupled converter system. For a given solar insolation, the tracking algorithm changes the duty ratio of the converter such that the solar cell array voltage equals the voltage corresponding to the MP point. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage corresponding to the MP point. Depending on the error and change of error signals, the fuzzy controller generates a control signal for the pulsewidth-modulation generator which in turn adjusts the duty ratio of the converter. The reference voltage corresponding to the MP point for the feedforward loop is obtained by an offline trained neural network. Experimental data are used for offline training of the neural network, which employs a backpropagation algorithm. The proposed peak power tracking effectiveness is demonstrated through simulation and experimental results. Tracking performance of the proposed controller is also compared with the conventional proportional-plus-integral-controller-based system. These studies reveal that the fuzzy controller results in better tracking performance.Index Terms-Coupled-inductor interleaved boost converter, feedforward loop, fuzzy controller, maximum power (MP) operation, neural network, proportional plus integral controller, solar cell array (SCA).

Photovoltaic Array Maximum Power Point Tracking Using Fuzzy Control Method

“…The well-known center of gravity method for defuzzification is used in this paper. It computes the center of gravity from the final fuzzy space, and yields a result which is highly related to all of the elements in the same fuzzy set [10]. The crisp value of control output U(k+1) is computed by the following equation:…”

Modeling and Simulation of Grid Connected PV Generation System Using Matlab/Simulink

“…Further, the efficiency of solar energy conversion to electrical energy is very low, only in the range of 9-17%. Therefore, maximum power point tracking (MPPT) is an essential part of a grid-tied solar PV system to ensurethat maximum available power is always extracted out of the PV panel at all conditions and steered to the AC grid, considered as an infinite sink of power ideally [3].This feature has an essential role in dynamic response andefficiency of the photovoltaic system, in literature, different MPPT algorithms are introduced and among them the "Perturb and Observe (P&O)" and "Incremental Conductance" are mostly used [4], [5], on the other hand, some MPPTs are more rapid and accurate and thus more impressive, which need special design and familiarity with specific subjects such as fuzzy logic [6], or neural network [7] methods.…”

Design and performance analysis of a PV solar powered water pumping system

“…In the case of the analogue MPPT control, there is a problem of congestion divergence of the electronic components used (Shraif, 2002;El Ouariachi et al, 2009). However, in the case of the digital MPPT control, many algorithms have been used in the literature (Hussein et al, 1995;Salas et al, 2006;Esram and Chapman, 2007), and include the incremental conductance method IC, (Cocconi and Rippel, 1990;Veerachary et al, 2003) the fuzzy logic method (Bahgat et al, 2005), the neural networks method (Swrup and Ansari, 2012) and the traditional perturbation and observation method, which poses precision problems on the regulation around the MPPT (El Ouariachi et al, 2009).…”