Improved higher order adaptive sliding mode control for increased efficiency of grid connected hybrid systems

Abstract

Abstract This paper proposes a hybrid learning algorithm based super twisting sliding mode control (STSMC) of a hybrid wind/photovoltaic (PV) power system for grid connected applications. The gating pulses of the voltage source converter (VSC) are generated by employing adaptive reweighted zero attracting least mean square (RZA-LMS) algorithm. The control law acquiring the super-twisting algorithm generates a continuous and saturated control signal to regulate a hybrid system influenced by disturbances. The proposed control injects sinusoidal currents into the grid with low total harmonic distortion (THD) which improves the steady state & dynamic performance of the system by mitigating power system problems like harmonic injections besides giving satisfactory results under dynamic loading, varying wind speeds and solar insolation. It is a chattering free control which enhances the quality of disturbance rejection and sensitivity to parameter variation. It also caters to abnormal conditions like voltage distortions, DC link variations and reduces the latter by a factor of 80 V besides reducing switch stress by a factor of 5 V. This control exhibits robustness against model uncertainties and external disturbances. Also, the loss component is reduced which decreases the unmodelled losses. It also ensures efficient power flow between the grid, hybrid source and the load. The efficacy of the system is verified in MATLAB/Simulink. Improvements are also observed during dynamic conditions in terms of reduced fluctuations, steady state error and peak overshoot.