Abstract
Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.
Highlights
Today, the fossil fuel resource is used in generation of energy
While the latter method suffers from the drift problem under rapid changes in atmospheric conditions, as shown in the zoomed part of Figure 13b, the problem can be seen as being minimal when compared to the perturb and observe (P&O)-Maximum power point tracking (MPPT)
An efficient MPPT technique based on adaptive neural-fuzzy inference system (ANFIS) using a real photovoltaic system data has been designed
Summary
The fossil fuel resource is used in generation of energy. This resource has caused global warming and air pollution due to the CO2 emissions from this fuel. Low efficiency is a major challenge when installing this resource, because the generated power from a PV array depends upon the solar irradiations (G) and operating temperatures (T) of climatic conditions, which can result in losses of energy of up to 25% [3]. The FLC-MPPT is classified as a high powerful controller for a PV system due to its faster tracking speed and lesser oscillation when compared with classical MPPT controllers [16] It does not require training data, unlike the ANFIS and ANN methods, resulting in its operating for different types of PV arrays with the same MPPT proposal. 2019, 8, 858 of 20 tracking the MPP and avoiding the drift phenomenon It achieved the highest efficiency compared with the FLC- and P&O-MPPTs. The rest of this paper is organized as follows.
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