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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 esti-mates 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 &nbsp;phasor trends to &nbsp;closely mimic natural disturbances aiming at &nbsp;trends- based applications. Also, the &nbsp;work proposes an un-supervised wavelet probability mapping method for &nbsp;the detection and correction of dynamic false data sequences. The proposed method identifies the attack sequences from disturbances using relative wavelet energy measure and &nbsp;replaces the &nbsp;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/Power-Factory 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.

Department of Electrical Engineering

International Journal of Electrical Power & Energy Systems

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

The existing power system utilizes communication infrastructure for fast and reliable transfer of control and protection inputs. This dependency of a power system on communication infrastructure for critical applications makes it vulnerable to cyber attacks. Cyber-induced outages trigger both randomized and intentional switchings in a power system producing relatively similar dynamics as natural events making their classification difficult. This article proposes a principal component analysis (PCA) assisted sequential deep learning approach for online classification of cyber outages and natural events in a power system. This article provides objective-driven models for false setting injection (FSI) and false command injection (FCI) type attacks. The proposed classification method uses PCA to deduce truncated z-score sequences [or principal test sequences (PTSs)] capturing distinct spatio-temporal progression patterns of natural disturbances and cyber outages. The PTSs in the training sets are shuffled and sampled using a stratified random sampling technique and classified using an ensemble long short-term memory network. The proposed method is tested for simulation examples of FSI and FCI attacks in the standard IEEE118-bus test system, where it showed improved accuracy and time performance.

IEEE Systems Journal

PCA-LSTM Learning Networks With Markov Chain Models for Online Classification of Cyber-Induced Outages in Power System

Increase penetration of intermittent renewable sources causes reduction and time variation in system inertia, forcing dynamic changes in system coherency status. Besides increased penetration levels, other factors like intermittent generation and change in distribution of renewables also influence the system coherency that needs to be addressed. This paper examines the impact of variability of renewables on system coherency and proposes a coherency identification method based on spectrum similarity approach. The proposed method uses discrete cosine Stockwell transform to derive multiple time-frequency (TF) features that captures pair-wise similarity between synchrophasor frequency signals. Thereafter, mean shift spectral clustering is used to cluster the buses with most homogeneous TF spectrum. The proposed method is validated with renewable generation in the IEEE-39 bus system and a real transmission system of India grid. © 1969-2012 IEEE.

World University root node

IEEE Transactions on Power Systems

A Spectrum Similarity Approach for Identifying Coherency Change Patterns in Power System Due to Variability in Renewable Generation

Real-time classification of events facilitates corrective control strategies, supervisory protection schemes, and on-line transient stability assessment of a power system. The synchrophasor-based event classification techniques face challenges like similar responses for different classes of events, i.e., inter-class similarity (ICS), applicability to limited classes of events, and moderate real-time performance for a large power system. In addition, the enhanced ICS effect of increased renewable penetration on events classification needs to be addressed. This paper proposes a kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data. The proposed method uses a diffusion type kernel density estimator (DKDE) to characterize the shape of 3-D voltage and frequency distribution along time in terms of probability density functions (PDFs). That have distinct scale, shape, and orientation for different classes of events. Thereafter, a set of statistical features is derived from PDFs to train a multi-layered deep neural network for event classification. The proposed method is validated for renewables in IEEE-39 bus system and real transmission system of India grid using DIgSILENT/PowerFactory and also on a real phasor measurement unit data for India grid, where it showed better performance for ICS and renewable integration cases. © 2010-2012 IEEE.

IEEE Transactions on Smart Grid

Real-Time Event Classification in Power System with Renewables Using Kernel Density Estimation and Deep Neural Network

Real-time multiple event analysis is important for reliable situational awareness and secure operation of the power system. Multiple sequential events can induce complex superimposed pattern in the data and are challenging to analyze in real time. This paper proposes a method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data. For detection and temporal localization, a Teager-Kaiser energy operator (TKEO) based method is proposed. For event classification, a time series classification based method using energy similarity measure (ESM) is proposed. The proposed method is tested for simulated multiple event cases in the IEEE-118 bus system using DigSilent/PowerFactory and real PMU data for the Indian grid. © 2005-2012 IEEE.

IEEE Transactions on Industrial Informatics

Real-Time Multiple Event Detection and Classification in Power System Using Signal Energy Transformations

2022 IEEE International Power and Renewable Energy Conference (IPRECON)

Synchrophasor Data based Disturbance Monitoring and Tier-based Spatial Localization

On increasing the penetration of DGUs in a predominantly resistive network, new low damping modes are generated hampering oscillatory stability.On increasing the virtual inertia injections in a predominantly resistive network, few oscillatory modes tend to shift towards right half of s-plane, thus hampering small signal stability.The importance of coherency based network control and partitioning in active distribution network two scenarios are considered in the work. In first scenario, voltage coherency based emergency control of distribution system and in second scenario, cases are discussed.In this work, the fundamental factors affecting the angle and voltage coherency criteria of active distribution system are defined for predominantly inductive and resistive networks which results duality in coherency.

International Journal of Electrical Power and Energy Systems

<a href="https://www.sciencedirect.com/science/article/pii/S0142061522007748">Fundamental factors influencing bus coherency in distribution networks with distributed energy resources</a>