A Hybrid of Fuzzy Logic and Sliding Mode Techniques for Photovoltaic Maximum Power Point Tracking Systems Under Partial Shading

Abstract

Solar energy harvesting using photovoltaic (PV) modules have been one of the most common sources of renewable energy for several decades. These modules have been used as a source of electricity for households, industries, in stand-alone, and grid-connected solar plants. The modules consist of semi-conductor solar cells combined in series and parallel. In order to make a solar system, the modules are usually linked in series. The performance of a solar system is affected by environmental factors like varying radiance and temperatures, shadowing caused by high-rise buildings, birds, fog, trees and dust. Such varying environmental conditions affect a solar cell's efficiency. Nevertheless, given all the effort made to mitigate the impact of all these environmental threats, some of the natural occurrences such as varying radiance, clouds, dust, wind-speed and change in temperature, can not be done away with. To improve the e ciency of the entire solar system, power extraction must be optimized under all weather conditions. Fuzzy logic and sliding mode techniques are e cient, fast and reliable methods of tracking the maximum power point that have been used in this study. The application of these two approaches substantially increases system e ciency for all environmental conditions including partial shading instances. The sliding mode technique is a very fast, stable and robust algorithm that work e ectively under very stable weather condition while the fuzzy logic has been exploited under partial shading conditions. Both methods rely heavily on a good understanding of the characteristics of PV modules, which are studied using I-V, P-V or P-I curves. In this work, three new algorithms have been used to simulate and model the characteristics of a PV module. The algorithms are based on a single diode equivalent circuit, which has been chosen due to the simplicity of simulation and modeling and provides a fast convergence time. The algorithms are classi ed according to the method of obtaining the best values of the unknown ve parameters of the diode model. Ideality factor (A), saturation current (Io), photocurrent iv (Iph), series (Rs) and parallel (Rp) resistances are the ve unknown parameters to be determined for characterization of a PV module using a diode model. These parameters have been extracted using the I-V curve's three critical points at short circuit point (SCP), open circuit point (OCP) and maximum power point (MPP). The rst algorithm has been based on the choice of ideality factor below the optimal ideality factor (Ao), such that 0 A Ao, whereas the other parameters depends heavily on the choice of A. The second algorithm has been based on the choice of ideality factor in the neighborhood of Ao and the third algorithm has been based on A Ao. The three methods have been utilized to characterize the solar module using I-V and P-V curves and have output power errors of less than 0.5%. For proof of concept of the three algorithms, PV module with IEC61215 speci cations have carefully selected from Kyocera- KC130CGT. Additional experimental work has been carried out at Solinc Kenya Ltd using Solinc 60Wp and 250Wp PV modules, similar to those mounted on the rooftop of the building in Chiromo at School of Physical Sciences.