Improvement of maximum power point tracking algorithm with adaptive neuro fuzzy inference system for solar energy systems

In this thesis, maximum power point tracking MPPT and DC-DC boost converter is presented. This design meet performance requirements, where fuzzy are suitable to surpass the performance of maximum power point tracking algorithm. Solar cell is considered as the basic element in solar photovoltaic system which is used to convert the sunlight into electrical energy. The performance of the solar cell depends on the environment and the weather conditions such as temperature and irradiation of the sun. These factors are fluctuating in the nature. Under the effect of the normal conditions, the photovoltaic produces unique maximum operating point. Maximum efficiency at load can be achieved when solar PV operates in optimum points. In order to achieve high efficiency from the solar PV system, MPPT can be used to obtain high efficiency. MPPT charge control technique should be utilized as a part of conditioning system between the DC load and PV module. MPPT is used to convert the incoming variable DC electrical energy from the solar PV into fixed DC and track the maximum power from the solar PV module. The maximum power point is fluctuating when the irradiation level and temperature are varied. For this reason, MPPT used to match the impedance between the load and the battery by changing duty cycle of the DC-DC converter. KEYWORDS:Maximum Power Point Tracking Algorithm, Adaptive Neuro Fuzzy Inference System, Solar Energy Systems