PITCH CONTROL is one of the major aspects of wind turbine CONTROL, particularly over high wind speed and oscillations. General Electric (GE) model of wind turbine is practically compatible with the structure of the wind turbines. It ha s bee n pr ove d t h at simulation results using this model are closer to the actual case, compared to other available models. Therefore, in this paper the GE model is used to evaluate the effectiveness of three different CONTROLlers including Fuzzy CONTROLler, self-organized Fuzzy CONTROLler (SOFC) and PI CONTROLler in PITCH CONTROL of the wind turbine. Afterward, the results of the CONTROLler applications as well as the no CONTROLler case in t h e PITCH CONTROL are compared. The results show a better performance of SOFC in damping the oscillations and overshoot of the wind turbine shaft speed. Finally, electrical power limit and converter cost, the economic analysis of PITCH CONTROLler application are carriedout. It is shown that the application of the SOFC results are around $142, 646 saving.

The effective utilization of wind energy conversion system (WECS) is one of the most crucial concerns for the development of renewable energy systems. In order to achieve appropriate wind power, different PITCH angle methods are used. Recurrent Adaptive Neuro-Fuzzy Inference System (RANFIS) is utilized in this paper in a new effective design to improve the performance of classical and adaptive Proportional Integral (PI) CONTROLlers applied for the PITCH CONTROL purposes. Adaptive-online performance and high robustness coverage are the main advantages of the suggested CONTROLler. The effectiveness of the proposed method is verified by a simplified two-mass wind turbine model and a detailed aero-elastic wind turbine simulator (FAST7). At any given wind speed, the proposed CONTROLler has outperformed PI, Adaptive Neuro-Fuzzy Inference System (ANFIS), and RANFIS based CONTROLlers, reducing the mechanical stress of drive train while presenting suitable aerodynamic power tracking and maintaining the rotational speed of the rotor under the rated value.

Wind energy is the most accessible source and is one of the fastest growing renewable energy systems. If this energy is exploited properly and well-suited to the turbine system, it would have the ability to compete economically and even be superior to other sources. For this purpose, in the high wind speed areas different methods are applied in the bases of harvesting as much wind energy as possible with respect to harness rotor speed at the rated value. In this paper, an extensive literature review on CONTROL and manufacturing strategies in power production and system performance of wind energy conversion system (WECS) has been highlighted. Classic CONTROL, adaptive non-linear CONTROL, robust CONTROL and intelligent CONTROL are among the CONTROL methods presented in this study. In comparing the CONTROLlers, although adaptive and robust CONTROLlers, with less sensitivity to changes in environmental conditions, outperformed the classic CONTROLler, and the intelligent CONTROLler presented better CONTROL flexibility and power quality through estimating the system variables and appropriate adaptation to changes at the operating point.

In this paper, an optimal adaptive robust PITCH CONTROLler is proposed for variable speed wind turbines (VSWTs). The proposed PITCH CONTROLler has stability analysis, while it simultaneously keeps the generated power of the wind turbine at the rated power and mitigates the mechanical loads on the gearbox. The proposed PITCH CONTROLler in this paper has two terms. The first term is a radial basis function neural network (RBFNN), to approximate unknown nonlinear functions of the wind turbine. Another term is a chattering-free continuous robust structure, which can cope with the approximation error. The weights of RBFNN and the gain of the robust structure are derived via the Lyapunov synthesis approach. It is proved that the closed-loop signals are semi-globally uniformed and ultimately bounded. The optimal parameters of the proposed CONTROLler are derived by solving a proposed multi-objective optimization problem using non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm. The effectiveness of the proposed CONTROLler is compared to the baseline PI CONTROLler designed by NREL. First, both the proposed and the baseline PI CONTROLlers are applied to the general model (2-mass model) of the wind turbine, and then they are validated via a highly reliable simulator called FAST. The results demonstrate the effectiveness and applicability of the proposed PITCH CONTROLler.

The variations of speech parameters due to emotion or stress are noticeable. In the presence of such variations, if a neutral model is used for the system, the speech recognition accuracy deteriorates. The evaluation of how emotion influences speech parameters is the first step towards emotional speech recognition. PITCH frequency is an important parameter in speech processing systems. Therefore in this research the effect of PITCH frequency and its slope due to emotion is explored for voiced phonemes. On the other hand, the influence of emotional state on continuous speech recognition performance is evaluated. The results show that the recognition performance of sentences with angry and happy states and also interrogative sentences has the most deterioration. This deterioration is more than 68% when compared to neutral speech recognition accuracy. To improve recognition results, we add the PITCH frequency information to the end of speech recognizer feature vector. The amount of improvement depends on the type of emotion and also added PITCH information. The results show that, PITCH frequency slope has a significant affect on the improvement of speech recognition accuracy even for neutral speech.