Wavelet probability distribution mapping for detection and correction of dynamic data injection attacks in WAMS

Abstract

The dependency of synchrophasor technology on shared/dedicated communication networks for data transfer and the lack of security specifications in C37.118 standard makes phasor measurements susceptible to data manipulation attacks. More critically, a false data injection attack could influence both accuracy of state estimates and the evaluation of system dynamics. Typically, false data sequences are modeled as multi-constraint non-linear optimization with random, step, or ramp type distribution. However, a carefully designed attack that mimics the dynamic response of a natural disturbance can hugely impact trends-based applications of synchrophasor-based disturbance monitoring and mode metering. This work designs a multi-channel/multi-sample dynamic injection attack in phasor trends to closely mimic natural disturbances aiming at trends-based applications. Also, the work proposes an un-supervised wavelet probability mapping method for the detection and correction of dynamic false data sequences. The proposed method identifies the attack sequences from disturbances using relative wavelet energy measure and replaces the corrupted sub-sequences with coherent un-corrupted trends using a kernel density estimation-based mode mapping method. The efficacy of the proposed method is validated for simulated injection cases in IEEE-118 bus system using DigSILENT/PowerFactory and real phasor data for the Eastern region of the India grid. The results show that the proposed method exhibits high detection accuracy and low correction error for different categories of dynamic false data cases under real system conditions.