STATES ESTIMATOR BASED ROBUST ADAPTIVE NONLINEAR CONTROL TECHNIQUE FOR MAXIMUM POWER POINT TRACKING OF STANDALONE WIND POWER SYSTEM

Abstract

Wind energy is one of the green and environment-friendly resources. However, harvesting a sufficient amount of energy from a wind energy plant depends on different components. While having variable wind speed, the maximum power extraction is the most significant component. But due to the increased insertion of wind energy into the electrical power systems, turbine controls are actively occupied in the research. The efficiency of the wind power systems has a significant impact on the energy zone, including industrial and commercial power. Sustainable energy resources, such as wind, may change the efficiency of the wind power system depending on environmental conditions such as buildings, weather, trees, and sea areas due to which wind speed variation occurs. In the literature, a variety of strategies have been adapted for this purpose (i.e., Power Signal Feedback (PSF), Tip Speed Ratio (TSR), the Hill Climb Searching (HCS) or Perturb and Observe (P&O), variable structure control scheme, nonlinear backstepping controller techniques, conventional feedback linearization, backstepping and Pole placement controllers). The main drawbacks are higher steady-state error, missing parameters, and lower dynamic response. Our major contribution is very significant and prominent in the present thesis work. On the one hand, we will design a nonlinear MPPT controller based on the nonlinear derived model, which is equipped with $3kW$ power having variable speed, fixed-pitch the so-called PMSG-WECS standalone power system. These controllers are Arbitrary Order Sliding Mode Control (AOSMC) and Fast Integral Terminal Sliding Mode Control (FITSMC). On the other hand, a very comprehensive comparative study will be carried out with the standard published results (i.e., feedback linearization and generalized global sliding mode control) to highlight the supremacy of our employed control strategies. Both our proposed control methodologies are robust compared to the conventional feedback linearization, backstepping and Poleplacemnt controllers. The reason is that we will be using the feed-forward neuralnetworks to estimate some of the nonlinear terms like drift terms and control input channels. One more interesting thing is that we will conduct in our work is the usage of high gain differentiator (HGO). The HGO will be used to estimate the higher derivatives of the outputs, which will be further used in the proposed control algorithm. Thus, the sensitivity to the sensor noises will be also be reduced via the use of the HGO. So, both our control methodologies are equipped simultaneously with neural networks and HGO blocks. Our control employment approach will be more practical than the standard literature. In addition, the adverse effects of the chattering phenomena (which is associated with sliding modes) and external disturbances will be reduced via the usage of neural networks and with the HGO usage. Hence, our controllers will be more appealing and capable enough to be used in practical scenarios. The efficiency of our proposed methods will authenticate in the simulation studies. To further validate these results, the results of the proposed control techniques are compared with standard literature results.