Young-Jun Kim, “태양광 발전을 위한 MPPT 비교분석,” 아주대학교 공학석사 학위 논문, 2013. > Paper

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Thesis Young-Jun Kim, “태양광 발전을 위한 MPPT 비교분석,” 아주대학교 공학석사 학위 논문, 2013.

2013

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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.

 

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