Red deer optimized recurrent neural network for the classification of power quality disturbance

Abstract

Power Quality Disturbance (PQD) in a power grid distribution destroys the quality of power to the user. Thus, early detection of disturbances in the power grid distribution is essential to diagnose the network before failure. Several disturbances in the power grid may cause voltage sag, voltage swell, or occurrence of both. In the proposed method deep recurrent neural network (DRNN) is used for classifying the PQD as well as Red Deer Optimization (RDO) algorithm is used for optimizing the weight from DRNN. Based on the behaviour of deer roaring rate will optimize the weight of DRNN from RDO. Signal processing is done by S-transform (ST) because of the better performance in signals detection in terms of a high order of noise. The proposed method is implemented in Simulink tool and the results are compared with the existing methods. The result shows that the power disturbances are classified with high accuracy of 99.95% and precision of 99.98% that are higher than the existing methods.