Real time implementation of artificial neural networks-based controller for battery storage supported wind electric generation

Energy storage systems have established their capability to overcome the problems caused by intermittent nature of renewable sources when integrated to existing grid. Voltage and frequency control, as well as load shifting can be done using grid level storage systems incorporated with renewable sources. They can effectively serve the system as an energy sink and source. Operational use of energy storage for grid level support of a wind electric generator (WEG) is demonstrated in this study. The battery storage supported WEG along with controllers is modelled. Artificial neural network controller, which is having inherent learning capability, is developed to regulate the power flow between wind generator and utility grid. The proposed algorithm and the corresponding controller are simulated in MATLAB/Simulink and implemented in the DSP processor. The real time data exchange between Simulink and the floating point DSP processor TMSF32028335 is realised using on-board JTAG emulator. The hardware implementation using DSP processor presented in this work establishes the efficacy of the proposed control strategy for real time applications.