Abstract
In the field of engineering when a situation is not resolved analytically, efforts are made to develop methods that approximate a possible solution. These efforts have originated the numerical methods known at present, which allow formulating mathematical problems that can be solved using logical and arithmetic operations. This paper presents a comparison between the numerical optimization algorithms golden section search and simulated annealing, which are tested in four different scenarios. These scenarios are functions implemented with a feedforward neural network, which emulate a partial shading behavior in photovoltaic modules with local and global maxima. The presence of the local maxima makes it difficult to track the maximum power point, necessary to obtain the highest possible performance of the photovoltaic module. The programming of the algorithms was performed in C language. The results demonstrate the effectiveness of the algorithms to find global maxima. However, the golden section search method showed a better performance in terms of percentage of error, computation time and number of iterations, except in test scenario number three, where a better percentage of error was obtained with the simulated annealing algorithm for a computational temperature of 1000.
Highlights
The codes were developed in C language and gnuplot was used to make the figures
Results obtained withwith the the algorithms forfor thethe testtest function
From the results obtained with the neural network, it can be concluded that it is important to have the structure results obtained with the neural network, be concluded is important to aFrom universal to approximate the test functions, sinceitacan minimum variation that in theitcomputation is achieved
Summary
Finding the extremes of multimodal functions has been a major research problem addressed by many researchers because the performance of most engineering optimization problems is like to that of systems with multimodal functions [1,2,3,4,5] One of these situations is partial shading that occurs in photovoltaic (PV) modules [6]. Objects interfering with the solar irradiance on the surface of the PV module cause that in the characteristic curve that usually has a single global maximum [6,7,8], there are multiple local maxima [9,10,11] This situation makes it difficult to implement maximum power point tracking (MPPT) controllers [12,13,14]. Four multimodal evaluation functions were used, which represent extreme situations of partial shading in photovoltaic (PV) modules
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