In an electrical distribution system heavy loads can greatly change the load current. In a weak distribution system voltage flicker occurs as a result of randomly changing reactive power demand of the random process loads such as periodically turning on and off of heavy loads. As a major power quality problem voltage flicker can be extremely harmful to sensitive loads that require stable voltage. In this study voltage flicker is analyzed using the Orthogonal Hilbert Huang Transform (OHHT) instead of Hilbert Huang Transform (HHT) which is a new signal processing method can be used in the analysis of nonlinear and non-stationary signals. Two kind of analysis are performed. In the first one the Orthogonal Intrinsic Mode Functions (OIMFs) of voltage flicker are obtained and compared with the Intrinsic Mode Functions (IMFs) that are not orthogonal. In the second one, Hilbert transform (HT) is applied to the second OIMF component which is obtained from the first analysis in order to find the amplitude and the frequency of flicker signal. For the comparison purposes the second analysis is also performed for the second IMF component which is not orthogonal. Results of these two analyses showed us that using the OIMF components instead of the IMF components which are not orthogonal reduced the error rate remarkably.

Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Shortterm power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from using perturb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system. The performance of the kernel-based prediction model depends on the availability of a suitable kernel function that matches the learning objective, since an unsuitable kernel function or hyper parameter tuning results in significantly poor performance. In this study for the first time in the literature both maximum power is obtained at maximum power point and short-term maximum power estimation is made. While evaluating the performance of the suggested model, the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used. The maximum power obtained from the simulated system at maximum irradiance was 852.6 W. The accuracy and the performance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error (RMSE) and mean square error (MSE) values. MSE and RMSE rates which obtained were 4.5566 * 10 −04 and 0.0213 using ANN model. MSE and RMSE rates which obtained were 13.0000 * 10 −04 and 0.0362 using SWD-FFNN model. Using SVR model, 1.1548 * 10 −05 MSE and 0.0034 RMSE rates were obtained. In the short-term maximum power prediction, SVR gave higher prediction performance according to ANN and SWD-FFNN.

This study presents a Digital Signal Processor (DSP) based embedded code generation which is obtained automatically in PSIM software for Permanent Magnet Synchronous Motor (PMSM) control system. The simulation model of the PMSM control system is developed in PSIM environment using Motor Control Blocks and Embedded Target for TI 28335 block. This control block diagram is send to SimCoder to generate C-code that is ready to run on the DSP hardware, SimCoder also creates the complete project files for the TI Code Composer Studio development environment where the code will be compiled, linked, and uploaded to the DSP using High Voltage Motor Control-PFC Kit. So, embedded code generation provides a very quick way to design a motor drive system from user specifications also programming greatly simplifies the generation, prototyping and modification of DSP based design, thus decreasing the development cycle time.

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