Improved Deep Neural Network (IDNN) with SMO Algorithm for Enhancement of Third Zone Distance Relay under Power Swing Condition

Abstract

A zone 3 distance relay is utilized to provide remote backup protection in the event that the primary protection fails. However, under stressful situations such as severe loads, voltage, and transient instability, the danger of malfunction in distance relay is relatively high since it collapses the system’s stability and reliability. During maloperation, the relay does not function properly to operate the transmission line. To overcome this problem, an advanced power swing blocking scheme has been developed. An improved DNN-based power swing blocking system is proposed to avoid the maloperation of the distance relay and improve the system’s reliability. The current and voltage signal of the system is sensed, and the sensed data is fed into the Improved Discrete Wavelet Transform (IMDWT). The IMDWT generates the coefficient value of the sensed data and further computes the standard deviation (SD) from the coefficient, which is used to detect the condition of a system, such as normal or stressed. The SD value is given to the most valuable algorithm for the improved Deep Neural Network (IDNN). In the proposed work, the improved DNN operates in two modes, the first mode is RDL-1 (normal condition), and the second mode is RDL-2 (power swing condition). The performance of the IDNN is enhanced by using the threshold-based blocking approach. Based on the threshold value, the proposed method detects an appropriate condition of the system. The proposed method is implemented in the Western System Coordinating Council (WSCC) IEEE 9 bus system, and the results are validated in MATLAB/Simulink software. The overall accuracy of the proposed method is 97%. The proposed method provides rapid operation and detects the power swing condition to trip the distance relay.