To increase the reliability of an off-grid hybrid renewable energy system (HRES). An Artificial neural network (ANN) controller for a dynamic voltage restorer is suggested in this paper (DVR). Solar PV array, wind, and fuel cells are considered as HRES in this work. Three boost converters, one for each source, connect these three sources to a central Dc-link. The MATLAB software tool is used to calculate the best size of the three suggested renewable sources. The DVR control is achieved by adjusting the load voltage under a variety of unusual working scenarios. Voltage sag, three phase fault, voltage imbalance and voltage swell are the three situations. Two PIs and One Ann are employed to regulate the D-Q axis voltage signals and modify the Gate pulses of the pulse width modulation (PWM) used to operate DVR. These D-Q axis coordinates represent the inputs to the ANN at any loading condition. The PWM pulses are represented by the outputs of the ANN. The input/output data is provided by automatic feature PI controllers, which had been introduced for managing the load voltage by DVR-PWM pulses under various abnormal operating parameters. The suggested ANN-DVR controller improves system performance by enhancing the voltage, current and power waveforms of each producing source. The system ensured that the three renewable sources continue to operate during faults by adjusting the defective line voltage, hence enhancing the low voltage ride through (LVRT) capabilities. Furthermore this, overall harmonic distortion is minimized.


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    Title :

    Power quality improvement of a hybrid renewable energy systems using dynamic voltage restorer with ANN controller


    Contributors:


    Publication date :

    2022-08-04


    Size :

    494006 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English