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In wind energy systems, it is necessary to have a good knowledge of the used generator and its parameters. Despite the motor manufacturer's specifications, accurately estimating the machine parameter reference values is critical for the perfect control system. In this context, off-line identification tests of a 3 kW permanent magnet synchronous machine were conducted to determine the mechanical and electrical parameters. In addition, this research exploits a meta-heuristic optimization technic dubbed grey wolf optimizer in the on-line parameter's identification field. The estimate method is focused on the parameter's estimation linked to a reference model. The rule is based on the cancelation of the current error among the measured and the associated estimated current. For each measurement, the grey wolf estimator adapts the reference model parameters until the model response and the accurate response be similar. All were implemented using the real-time interface of the DSpace DS1104 Controller card while using the ControlDesk graphical user interface and the Simulink/MATLAB environments. Results from hardware simulation confirm the efficacy and benefits of the suggested strategy. [ABSTRACT FROM AUTHOR]

The current study proposed a robust sensor-less sliding mode second-order based on a super twisting algorithm (STA-SMSO) approach using a new observer Model Reference Adaptive System-Adaptative Neuro-Fuzzy Inference System (MRAS-ANFIS). This model was applied to a doubly fed induction generator (DFIG) wind turbine running under variable wind speed and DFIG fed with a power voltage source without a speed sensor, while the control objective was used to regulate independently, the active and reactive power DFIG stator were decoupled by using the field-oriented control technique. Additionally, this process reduced the cost of the control scheme and the size of DFIG by eliminating the speed sensor (encoder). In order to improve the traditional MRAS, the MRAS-ANFIS observer was proposed to replace the usual PI controller in the adaptation mechanism of MRAS with an Adaptative Neuro-Fuzzy Inference System (ANFIS) controller. The estimation of rotor position was tested and discussed under varying load conditions in low, zero, and high-speed region. The results mentioned that the proposed observer (MRAS-ANFIS) presented an attractive feature, such as guarantees finite time convergence, good response on speed wind variations, high robustness against machine parameter variations, and load variations compared to the conventional MRAS observer and MRAS-Fuzzy. Hence, the estimated rotor speed converged to their actual value has the capacity for estimating position in deferent region (low/zero/high) of speed. [ABSTRACT FROM AUTHOR]

Mast structure is one of the most important parts of a vertical-axis wind turbine which supports generator and rotor and represents one-third of the overall costs in the production of a standard wind turbine (approximately 30%). All this may cause significant economic and physical losses when it is damaged or collapsed. The purpose of this research is to investigate numerically the static strength and structural dynamic responses of 10-kW vertical-axis wind turbine masts subjected to the aerodynamic and gravity loadings (according to the IEC 61400-2:2006 and EN 1991-1-4:2005 standards) using the SolidWorks finite element software. Mast structures with four different heights (12, 14, 16, and 18 m) and three various outer diameters (0.6, 0.7, and 0.8 m), in each height configuration, were evaluated. These analyses were performed to identify the stiffness, resistance, reliability, and natural frequency stiffness requirements within the mast structures, in order to save manufacturing cost. Based on static analysis, no structural failure is predicted for all masts during wind turbine operation according to maximum von Mises stresses at the bottom of the mast and maximum total deflections on the top of the mast. In addition, the dynamic parameters of these 12 models of masts have been studied to obtain the natural frequencies and corresponding mode shapes. Finally, the recommendations to avoid resonance and design strategy for each mast model are discussed. [ABSTRACT FROM AUTHOR]