In this study, a novel photovoltaic power forecasting system that utilizes a deep Convolutional Neural Network (CNN) structure and an input signal decomposition algorithm is proposed. The proposed CNN architecture extracts deep features to forecast short-term power using transfer learning-based AlexNet. The historical power, solar radiation, wind speed, and temperature data are selected as the input. The signal decomposition algorithm called Empirical Mode Decomposition (EMD) is utilized to decompose the historical power signal into sub-components. In order to extract deep features, all input parameters are converted to 2D feature maps and feed to the input of the CNN. The experiments are realized on a grid-tied Photovoltaic Power Plant (PVPP) that has 1000 kW installed capacity located in Turkey. The experiments are performed under four weather conditions as partial cloudy, cloudy-rainy, heavy-rainy, and sunny days to show the effectiveness of the proposed method. The obtained results are compared with the benchmark regression algorithms. When the results are analyzed, the proposed method gives the highest Correlation Coefficient (R) and the lowest Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and SMAPE values under all horizons and weather conditions. For 1-h to 5-h ahead, the average R values of the proposed method are obtained as 97.28%, 95.77%, 94.49%, 93.61%, and 92.62%, respectively. The average RMSE values are observed as 4.90%, 6.30%, 7.50%, 8.00%, and 9.17% for 1-h to 5-h ahead. The experimental results confirm that the proposed method outperforms the conventional regression algorithms and reveals effective results with its competitive performance.

Power Quality Improvement Using Hybrid Passive Filter Configuration for Wind Energy Systems Wind energy;Power quality;PWM rectifier;WECS; Wind energy conversion systems (WECS) which consist of wind turbines with permanent magnet synchronous generator (PMSG) and full-power converters have become widespread in the field of renewable power systems. Generally, conventional diode bridge rectifiers have used to obtain a constant DC bus voltage from output of PMSG based wind generator. In recent years, together advanced power electronics technology, Pulse Width Modulation (PWM) rectifiers have used in WECS. PWM rectifiers are used in many applications thanks to their characteristics such as high power factor and low harmonic distortion. In general, L, LC and LCL-type filter configurations are used in these rectifiers. These filter configurations are not exactly compensate current and voltage harmonics. This study proposes a hybrid passive filter configuration for PWM rectifiers instead of existing filters. The performance of hybrid passive filter was tested via MATLAB/Simulink environment under various operational conditions and was compared with LCL filter structure. In addition, neuro-fuzzy controller (NFC) was preferred to increase the performance of PWM rectifier in DC bus voltage control against disturbances because of its robust and nonlinear structure. The study demonstrates that the hybrid passive filter configuration proposed in this study successfully compensates current and voltage harmonics, and improves total harmonic distortion and true power factor.

In this study, wind power forecasting is performed for a Wind Power Plant (WPP) with an installed capacity of 135 MW in Turkey. The ruggedness index (RIX) of the terrain where WPP was installed is analyzed with Wind Atlas Analysis and Application Program (WAsP). According to the obtained RIX value, the terrain of WPP is found to be complex. Due to the complexity of the terrain, wind power forecasting becomes difficult. To deal with this problem, a forecasting method with fast, accurate, and high performance is needed. Therefore, Extreme Learning Machine (ELM) based method is proposed for wind power forecasting in this study. Electrical and meteorological measurements are obtained from WPP for the application of the proposed method. These measurements are provided with high quality measuring devices. Also, Global Positioning System (GPS) time synchronization is used to prevent lags between measurements. The wind speed, wind direction, and wind power data of 1-year period are obtained from WPP. These data are used to compare the proposed method with a classical Artificial Neural Network (ANN) based method in terms of two, three and four hours-ahead wind power forecast performances. In the forecast studies performed for all data related to 2, 3, and 4-hours ahead, Normalized Root Mean Square Error (NRMSE) values of ELM are obtained as 7.01, 10.12, and 12.06, respectively, while these values are found as 8.19, 12.18, and 13.09 for ANN. In addition, the values of Correlation Coefficients (R) of the proposed forecast method results regarding 2, 3, and 4-hours ahead are 0.96588, 0.93528, and 0.88984, respectively. The R values related to ANN are observed as 0.95421, 0.91373, and 0.87576, respectively. According to the obtained results, it is observed that ELM has better performance features than classic method under all forecast conditions and it is clearly seen that ELM has by far short training time than other one.

During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission system operators and wind power plant owners. To solve this problem, numerous wind power forecast systems have been developed. Unfortunately none of them can obtain absolutely accurate forecasts yet. Thus, researchers assumed that wind power generation is a stochastic process and they proposed a stochastic programming approach to solve problems arising from the uncertainty of wind power. It is well known that representing stochastic process by possible scenarios is a major issue in the stochastic programming approach. Large numbers of scenarios can represent a stochastic process accurately, but it is not easy to solve a stochastic problem that contains a large number of scenarios. For this reason scenario reduction methods have been introduced. Finally, the quality of this reduced scenario set must be at an acceptable level to use them in calculations. All of these reasons have encouraged authors to develop a wind power scenario tool that can generate and reduce the scenario set and test the quality of it. The developed tool uses historical data to model wind forecast errors. Scenarios are generated around 24 day-ahead point wind power forecasts. A fast forward reduction algorithm is used to reduce the scenario set. Two metrics are proposed to assess the quality of the reduced scenario set. Site measurements are used to test the developed wind power scenario tool. Results showed that the tool can generate and reduce the scenario set successfully and the proposed metrics are useful to assess the quality.

It is well-known that rectifiers are used widely in many applications required AC/DC transformation. With technological advances, many studies are performed for AC/DC converters and many control methods are proposed in order to improve the performance of these rectifiers in recent years. Pulse width modulation (PWM) based rectifiers are one of the most popular rectifier types. PWM rectifiers have lower input current harmonics and higher power factor compared to classical diode and thyristor rectifiers. In this study, neuro-fuzzy controller (NFC) which has robust, nonlinear structure and do not require the mathematical model of the system to be controlled has been proposed for PWM rectifiers. Three NFCs are used in control scheme of proposed PWM rectifier in order to control the dq-axis currents and DC voltage of PWM rectifier. Moreover, simulation studies are carried out to demonstrate the performance of the proposed control scheme at MATLAB/Simulink environment in terms of rise time, settling time, overshoot, power factor, total harmonic distortion and power quality.