The transition of power systems toward digital substations has brought numerous advantages to substation automation. However, this digital transformation exposes substations to various cyberattacks. Thus, ensuring the integrity and availability of power system data has emerged as a critical concern in modern power system networks. A crucial cybersecurity concern is pilot protection, with its security being of utmost importance in bulk power system networks to safeguard against significant disturbances and blackouts, as well as to facilitate fast fault-clearing operations. This paper introduces a dynamic state estimation (DSE) technique to supervise the operation of pilot protection scheme. The proposed scheme accurately estimates transmission line impedance and uses this estimation to supervise the legacy pilot protection scheme. The method employs network physical laws, sampled value measurements, and an Unscented Kalman Filter (UKF) technique to enhance the cybersecurity aspects of the pilot protection scheme. Additionally, the cybersecurity of the DSE-based pilot protection supervision scheme is evaluated against cyberattacks such as denial of service (DoS) and false data injection (FDI). The simulation results, validated using the IEEE 9 bus test system, demonstrate the effectiveness of the proposed method for pilot protection supervision.

This paper presents an empirical wavelet transform (EWT) based approach to perform postmortem analysis of wide-area measurement (WAM) based signals. The commonly used empirical mode decomposition (EMD) has limitations such as mode mixing, sensitivity to noise, and sampling rate. The decomposition provided by EWT is more consistent as compared to EMD. The modes revealed by the EWT help in extracting dynamic patterns of different power system disturbances. The dynamic patterns extracted through EWT-based decomposition are further used as inputs to a data-mining tool known as random forest, to build a wide-area disturbance classifier (WADC) model. The efficient mode extraction quality of the EWT-based signal processing tool is analyzed for WAM data recorded on Northern Grid of Indian Power System. The performance of the WADC is validated on IEEE 39-bus New England test system. The results provide improved performance in terms of decomposition quality and classification accuracy.

This paper presents a data-mining-based intelligent differential relaying scheme for transmission lines, including flexible ac transmission system device, such as unified power flow controller (UPFC) and wind farms. Initially, the current and voltage signals are processed through extended Kalman filter phasor measurement unit for phasor estimation, and 21 potential features are computed at both ends of the line. Once the features are extracted at both ends, the corresponding differential features are derived. These differential features are fed to a data-mining model known as decision tree (DT) to provide the final relaying decision. The proposed technique has been extensively tested for single-circuit transmission line, including UPFC and wind farms with in-feed, double-circuit line with UPFC on one line and wind farm as one of the substations with wide variations in operating parameters. The test results obtained from simulation as well as in real-time digital simulator testing indicate that the DT-based intelligent differential relaying scheme is highly reliable and accurate with a response time of 2.25 cycles from the fault inception.