Nowadays, solar or photovoltaic energy is the most commonly used renewable energy resources in the world. Despite its advantages such as freely available, low maintenance cost, pollution-free, inexhaustible, and reliable, its low conversion efficiency is a major drawback. To increase the efficiency of the photovoltaic system, all photovoltaic modules in the array must be operated at maximum power point. Therefore, maximum power point tracking technique is used for predicting and tracking the maximum power point. In the literature, maximum power point tracking techniques are generally classified as soft computing and conventional. Soft computing techniques are more preferred from both of them, because they can accurately track maximum power point of photovoltaic systems. In this study, an extensive review of soft computing based maximum power point tracking techniques under partial shading conditions until today is presented. The techniques are compared from the point of photovoltaic array dependency, sensors required, tracking efficiency, tracking speed, algorithm complexity, and oscillation around maximum power point.

In this study, the wind energy characteristics of Dinar region were researched. Required wind data was provided by the Dinar Meteorology Station of the Turkish State Meteorology Service. Wind data was recorded hourly between June 2015 and May 2020. The two variable Weibull probability distribution function was used to research wind characteristics of the region. Six different methods were used to calculate shape (k) and scale (c) variables of the probability distribution function. These are moment, graph, Justus empirical, energy trend, energy pattern and maximum likehood estimation methods. The performance of these methods were compared with four different statistical error analysis methods. These are root mean square error (RMSE), coefficient of determination (R2), chi-square error (X2) and mean absolute error (MAE). According to the results, maximum likehood estimation and energy trend methods have been the most successful methods in the calculation of Weibull parameters. In addition, this study is an important preliminary study in determining the wind energy characteristic of the region.

Power converters are required to work faster and with higher power density with the developing technology. Therefore, the converter is expected to work in more than one direction. Usage of Dual Active Bridge DC-DC Converter is an example. To increase the power density of the converters, it is necessary to increase the switching frequency. In conventional Si MOSFET based converters, power losses are very high and cause high electromagnetic interferences at high frequencies. These disadvantages lead developers to the use of wide-band gap semiconductor based converters such as SiC However, SiC MOSFETs will also emit electromagnetic interference (EMI) above a certain frequency. In this study, the EMI, emitted at certain frequencies by the Dual Active Bridge (DAB) DC-DC Converter, is simulated by the LTspice. It was observed that the Si-based inverter parts of the DAB converter generate 10 V EMI on the linear base, that means 140 dBµV EMI on the logarithmic base, at 20 kHz. The SiC-based converter does not emit any noise at the same frequency. However, when the frequency was increased to 250 kHz, it was determined that the SiC based converter emitted 2.3 V noise on the linear base, thus 123 dBµV noise on the logarithmic base. This study shows that not only Si MOSFET’s but only SiC MOSFET’s emit EMI over a certain frequency.