Yadav, Ravi

The disturbance monitoring, event sequence recording, and automated fault analysis in the power system require processing of power quality and digital fault recorder data. Through, reliable and granular data streaming/storage services, which inadvertently introduces unwanted data quality issues like data gaps or missing samples. The work proposes a data-driven, gap length, and spectral change independent missing harmonic recovery method using static and dynamic selectivity criteria of spectral continuity and rate of subspace affinity change. For static selectivity, a novel mean-shift cross-energy operator is proposed that quantifies the spectral similarity between the static snapshots of signals across the gap. For dynamic selectivity, a novel rate of subspace change method is proposed to detect the subspace change points in a dynamically changing data set. Based on the selectivity criterion, the missing harmonic parameters are estimated and filled using the rotational invariance technique. The proposed method could effectively reconstruct the power signals with longer data gaps, contiguous, and randomly gaped data sets under dynamic harmonic conditions. The proposition is tested with simulated data sets in Matlab/Simulink and real system data from the India grid.

With the swift advancement of smart sensors and the incorporation of communication techniques, power systems become vulnerable to cyber-attacks. Robust system monitoring is imperative for ensuring reliability and for monitoring the op-erating state of the system, and for this purpose state estimation is (SE) used. Thus any falsification in the measurement sets of SE leads to disturbing the stable power system operation. By eluding identification through commonly used residue-based tests for detecting false data, any attacks can damage the power system security. This paper illustrates about the topology attack, any dis-turbance (adding or removing) in topology can affect the normal function of security-constrained economic dispatch (SCED). The objective of this research to execute a topology attack while cir-cumventing prevailing techniques for bad measurements i.e. bad data detection (BDD). Moreover, we propose a Multi-objective optimization framework to ensure minimal manipulation in the measurement set for maximizing the errors in SCED. Firstly, we develop an analytical model to evaluate state matrix violation with topology attacks. Also, matrix violations are masked by adding an optimal set of attack vector in the measurement set. The selection of an optimal candidate for a topology attack should be minimal to avoid disturbance in other applications related to SE (contingency analysis and optimal power flow). Some constraints are also considered: topology constraints, power flow constraints, and security constraints. This paper validates the efficacy of these attacks through comprehensive simulations utilizing various power system configurations.

The power system security becomes complex for N-x contigency situations, especially if triggered by cyber or physical attacks. This paper proposes a multi-objective combinatorial optimization problem of binary linear knapsack to model peak load loss in power network with least number of line trippings. The optimization problem represents the power network as a edge-weighted digraph and minimizes the number of link rejections to cause maximum loss of path gain with congestion and path flow constraints. The model is defined for complete information exploits with no restrictions on link selection and limited information model with boundary edge restrictions. The multi-objective optimization is solved using E-constraint and weighted sum methods. For both methods, approximate Pareto fronts obtained using multi-start techniques are used. The proposition is tested for the theoretical interconnected graph models and attack area in a IEEE-39 bus test system.