Super twisting sliding mode-type 2 fuzzy MPPT control of solar PV system with parameter optimization under variable irradiance conditions

The need for renewable energy sources is increasing while the use of non-environmentally energy sources is decreasing day by day. At this point, solar energy which is a great natural energy source comes to the fore. In this study, MPPT control is performed based on sliding mode control as a robust control method. A super-twisting sliding mode controller has been developed and type 2 fuzzy set has been adapted to the system to reduce the chattering problem. The developed MPPT control algorithm is applied to a solar PV system and tested under variable irradiance conditions. In addition, the parameters of super twisting sliding mode and type 2 fuzzy set are optimized. The efficiency of the proposed MPPT algorithm is presented with tables and graphics that contains sliding mode MPPT, super twisting sliding mode MPPT and super twisting sliding mode-type 2 fuzzy MPPT methods. The results show the MPPT efficiency of proposed system with a robust structure

Performance comparison of different machine learning algorithms on the prediction of wind turbine power generation

Over the past decade, wind energy has gained more attention in the world. However, owing to its indirectness and volatility properties, wind power penetration has increased the difficulty and complexity in dispatching and planning of electric power systems. Therefore, it is needed to make the high-precision wind power prediction in order to balance the electrical power. For this purpose, in this study, the prediction performance of linear regression, k-nearest neighbor regression and decision tree regression algorithms is compared in detail. k-nearest neighbor regression algorithm provides lower coefficient of determination values, while decision tree regression algorithm produces lower mean absolute error values. In addition, the meteorological parameters of wind speed, wind direction, barometric pressure and air temperature are evaluated in terms of their importance on the wind power parameter. The biggest importance factor is achieved by wind speed parameter. In consequence, many useful assessments are made for wind power predictions

Output Regulation-Based Optimal Control System for Maximum Power Extraction of a Machine-Side Power Converter in Variable-Speed WECS

In this study, the integral linear quadratic regulator (LQR) with servomechanism for machine-side power converter in PMSG-based variable-speed wind energy conversion systems (WECSs) has been proposed. The solution of the algebraic Riccati equation (ARE) has been found for the extended dimension of the state space equation of the system. The state vector has been extended with the integral of the angular shaft speed of the permanent magnet synchronous generator (PMSG) to penalize the errors. The maximum power tracking point (MPPT) algorithm is achieved by minimizing tracking errors between the angular shaft speed reference based on wind speed estimation and its actual values in the variable speed WECS. Also, the estimated aerodynamic torque is used to define the reference electromagnetic torque. This is possible when WECS is partially loaded and pitches angles are fixed at the position to generate maximum power. The mean absolute percentage error of the angular shaft speed of the PMSG-based WECS has been reduced by more than 71% under model uncertainty and noise presented case than in the traditional disturbance observers-based compensation scheme. While the disturbance observers for estimation model uncertainty are eliminated, the use of the high order disturbance observer for aerodynamic torque estimation proved to be necessary to enhance the reliability of wind speed sensors and hence the whole WECS