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 We analyzed the pro sand cons of the most frequently used MPPT algorithms in this paper. In a solar power module, energy output depends on external temperature and the amount of sunlight. Controlling this out put is necessary to achieving minimal energy loss while at the same time maximizing electrical power. Maximum Power Point Tracking (MPPT) is a technique that attempts to maximize power out put based on external conditions. MPPT may utilize one or more various methods, such as constant voltage control, Perturbation and Observation (P&O), Incremental Conduction (Inc Cond), “fuzzy” logic, neural networks, and the Newton method. Constant voltage control is an algorithm that varies the amount of current in the module to achieve constant voltage out put. This is simple to achieve; however, if the amount of solar radiation changes rapidly, it may not find the maximum electric power, decreasing efficiency. P&O continually adjusts the voltage out put of photo voltaic cells to maximize power out put, and its method is fairly simple and accurate. However, depending on external conditions, it may cause oscillations around the maximum power points and fail to show the maximum electric power. Because of this, Inc Cond is often used in conjunction with P&O when power output is at or near the maximum power point, Inc Cond achieve sits goal when the derivative of the power curve equals zero and it can track the maximum power point. The fuzzy logic method uses control rules based on expert knowledge, which makes it highly tolerant to noise. The control rules define relationships between input and out put voltage to be used when mathematical calculations can not produce accurate levels. However, this method can be very processor intensive if mathematical algorithms fail routinely. Neural network methods perform MPPT by “learning”: information is saved and used later. However, this method is also computationally expensive, so low-CPU systems can not use this method. The Newton method is the most efficient way to approximate the actual power function. However, as the slope of the power curve approaches zero(the peak), the method becomes increasingly complex.