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

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.

Automated summary

Power Quality Disturbance (PQD) in power grid distribution can degrade the power quality for users. Therefore, timely detection of disturbances in the power grid is crucial for diagnosing network failures. In this study, a deep recurrent neural network (DRNN) is used to classify PQD, and the Red Deer Optimization (RDO) algorithm is employed to optimize the weights of DRNN. By considering the behavior of deer roaring rate, RDO optimizes the weights of DRNN. Signal processing is conducted using S-transform (ST) due to its superior performance in detecting signals with high noise levels. The proposed method is implemented in Simulink tool and compared with existing methods, demonstrating higher accuracy (99.95%) and precision (99.98%) in classifying power disturbances.