Replaying outdated or uncorrelated data can lead to a distorted view of the actual system state, resulting in suboptimal control decisions that potentially compromise grid stability, reliability, and efficiency. Replay Attacks (RA) are a kind of stealthy attacks which can mar one of the very key application of the Energy Management System (EMS), viz., Power System State Estimation (PSSE). This paper, therefore, proposes a novel scheme to make the PSSE resilient against RAs. To this end, first, a Power Transfer Distribution Factor (PTDF)-assisted vulnerability analysis is carried out to identify the critical SCADA measurements which may become a preferred choice of the attacker to launch RAs. Next, an optimal number of secured phasor measurements are exploited to detect and correct any RA in the SCADA measurement set, followed by a simple hybrid estimation scheme to reconstruct the falsified set of measurements. The proposed strategy’s effectiveness is validated through testing on three standard IEEE test systems, namely IEEE 14, New England (NE) 39, and IEEE 118 using simulated data generated from Real-time Digital Simulators (RTDS) and MATPOWER. Finally, the efficacy, feasibility and robustness of the proposed method under different variant of RA scenarios are compared with two existing Kalman Filter-based distributed state estimation methods associated with the three widely used statistical cyber attack detectors. Note to Practitioners—Power System State Estimation (PSSE) is a key application in the EMS, which further caters to many important real-time grid applications. If the SCADA measurements, which are fed to the PSSE program get corrupted with the Replay Attacks (RAs), it can jeopardize the smooth functioning of the power grids. Being motivated by this, this article presents a simple approach to detect and mitigate RAs in PSSE. The proposed scheme exploits a set of limited secured phasor measurements, along with the vulnerable SCADA measurements, in the form of Hybrid State Estimation (HYB-SE) to detect and correct any presence of RAs. The proposal is validated on the IEEE 14, NE 39, and IEEE 18 bus systems, modelled in Real Time Digital Simulator with a maximum detection accuracy of 94.6%, 94.2%, and 84.47% respectively. The superior performance of the proposed detection approach is also validated against two existing distributed state estimation methods accompanied with three statistical cyber attack detectors.

This article proposes a novel approach to identify the critical attack path (CAP), comprising of multiple cyber and physical components that an attacker may exploit sequentially, so as to cause maximum damage to the system. The proposed CAP detection framework (CAPDF) considers the jamming attack, which causes cascading failures on the individual cyber and physical layers. The framework involves three stages wherein,Stage-1identifies the critical physical lines and cybernodes in the system. Due to the interdependence of the cyber and physical components, the cascaded effect of a critical component’s outage is evaluated inStage-2. Finally,Stage-3ranks the critical contingency sequence based on the multifactor decision analysis. Further, the proposed CAPDF is made robust against the stochastic nature prevailing in the system conditions, using a stochastic model and an ensembling mechanism. The proposed scheme is implemented using a MATLAB+NS3 co-simulation platform on the IEEE 14-bus system and the New England 39-bus system.

Phasor Measurement Units (PMUs) are state-of-the-art measuring devices that capture high-resolution time-synchronized voltage and current phasor measurements in wide area monitoring systems (WAMS). Their usage for various real-time applications demands a huge amount of data collected from multiple PMUs to be transmitted from the local phasor data concentrator (PDC) to the control centre. To optimize the requirements of bandwidth to transmit the data as well as to store the data, an efficient synchrophasor data compression technique is desired. To this end, this paper presents a 3-stage data compression scheme in which Stage-1 performs the accumulation of the data matrix from the optimally placed PMUs in WAMS into the local PDC. The data is then passed through a novel Ramanujan's sum-based fault window detection algorithm to identify the fault within the PMU data matrix in Stage-2. Finally, Stage-3 proposes an enhanced graph filtering-enabled principal component analysis scheme which expands the notion of conventional PCA techniques into the graph domain to compress the data. The performance of the proposed scheme is verified on the IEEE 14-bus system and New England 39-bus system. Further, practical applicability of the proposed method is validated on field PMU data collected from EPFL campus in Switzerland.

This article proposes a novel three-step framework to accurately detect, estimate and mitigate cyber-attacks like unauthorized data manipulation and hijacking controller attacks which can jeopardize the entire frequency and voltage stability of an autonomous microgrid (MG). Step-1 proposes a novel maximum mean discrepancy (MMD)-based index to detect and locate the attacked distributed energy resources (DERs); in Step-2, an unknown input observer (UIO) is proposed to coarsely estimate the unknown false data injection attack (FDIA) parameters; and Step-3 develops a backstepping integrated sliding-mode control (BSMC) to compensate the attack by injecting reverse control input bias. The efficacy of the proposed cyber-attack detection and mitigation framework is rigorously tested under various types of cyber attacks on the modified IEEE-13 bus distribution test feeder operated in an islanded mode, modeled in RSCAD and is validated with real-time digital simulator (RTDS). The performance and superiority of the proposed detection scheme are compared with an existing method through hardware-in-the-loop (HIL) simulation control environment.

This article proposes an energy management model for residences in an inclining block rate (IBR) tariff environment. Given acluster, i.e., a group of appliances that satisfy certain requirements, the proposed scheme is primarily driven by two layers of clustered priorities. The first layer of clusters comprises various services of the residences and governs it with shareablez1priorities, and the second layer of clusters binds the major power-consuming appliances under each substation and regulates it with dynamically changingz2priorities. Integer linear programming is used to solve the multicriteria decision problem crafted to address both grid-connected and isolated modes of operations. The model is tested on MATLAB using the IEEE 123-bus system with the modified Pecan Street dataset. Various results reveal that the proposed model is effective in providing a reliable power supply to the critical loads in all the scenarios while keeping the price within the preference limit with minimum comfort-loss for end-users, even in the presence of nonresponsive IBR environment.

Measuring the power consumption of individual household appliance is an essential task for home energy management and demand response programmes. To this end, this article proposes a simple yet effective, two-stage nonintrusive appliance load monitoring scheme. A standard deviation based automatic state detection algorithm is developed in stage-1, which results in the information about states and the transient spans of individual appliances. In stage-2 of the proposal, a penalty-based enhanced integer programming (IP) based load disaggregation method is proposed. The efficacy of the proposed method is initially verified with the reference energy disaggregation data dataset and, then, tested on an actual residential house. Various test results reveal that the proposed enhanced IP-based method is an effective and less complex solution to load disaggregation problem.

This article proposes a novel technique for fundamental and selective harmonic phasor estimation of a single-phase voltage signal based on open-loop voltage signal processing approach, under adverse distorted conditions. At heart, a modified fast discrete Stockwell transform (FDST) is used for the phasor estimation. The performance of the FDST is improved by incorporating a new frequency-dependent Gaussian window for capturing the time-localized variations of individual frequency component. The modified window has four parameters, which are selected based on the statistical properties of the given input voltage signal. Furthermore, under off-nominal frequency conditions the spectral leakage problem, faced by the S-transform, is avoided by proposing a modified sample value adjustment based preprocessing stage. Various static and dynamic tests were considered according to the IEEE Standard 1159, IEEE Standard C37.118.1a-2014, and EN-50160 standards under harmonics and interharmonics scenarios. The performance of the proposed method is validated using synthetic voltage signals, simulated voltage signals on 6-bus microgrid model developed in power system computer aided design (PSCAD), and hardware implementation on dSPACE 1103. The rigorous test cases reveal that the proposed phasor measurement unit has a good adaptability to grid frequency drifts, a good harmonic and dc-offset rejection capability, and a very good steady state as well as dynamic response.