Edinburgh Napier University

  Most conventional Fuzzy Logic Controller ( FLC ) rules are based on the knowledge and experience of expert operators: given a specific input, FLCs produce the same output. However, FLCs do not perform very well when dealing with complex problems that comprise several input variables. Hence, an optimization tool is highly desirable to reduce the number of inputs and consequently maximize the controller performance, leading to easier maintenance and implementation. This article, presents an enhanced fuzzy logic controller applied to a photovoltaic system. Specifically, both inputs and membership functions are reduced, resulting in a Highly Reduced Fuzzy Logic Controller ( HRFLC ), to model a 100kW grid-connected Photovoltaic Panel ( PV ) as part of a Maximum Power Point Tracking ( MPPT ) scheme. A DC to DC boost converter is included to transfer the total energy to the grid over a three-level Voltage Source Converter ( VSC ), which is controlled by varying its duty cycle. FLC generates control parameters to simulate different weather conditions. In this study, only one input representing the current variation ( △I ) of the FLC is used to provide an effective and accurate solution. This reduction in simulation inputs results in a novel HRFLC which simplifies the solar electric system design with output Membership Functions ( MFs ). Both are achieved by grouping two rules instead of using an existing state-of-the-art method with twenty-five MFs . To the best of our knowledge, this is the first FLC able to provide such rules compression. Finally, a comparison with different techniques such as Perturb and Observe ( P&O ) shows that HRFLC can improve the dynamic and the steady state performance of the PV system. Notably, experimental results report a steady state error of 0.119%, a transient time of 0.28s and an MPPT tracking accuracy of 0.009s.