Detection of PMU spoofing in power grid based on phasor measurement analysis

Yasinzadeh, Mohammad; Akhbari, Mahdi

doi:10.1049/iet-gtd.2017.1445

Cited by 29 publications

(12 citation statements)

References 20 publications

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“…The nonobservable phasors of the system could be observable with few extra PMUs. A binary search algorithm is exploited to explain the PMUs sites optimally, which would permit the complete network to be observable under usual operating events [23].…”

Section: A Pmu Placementmentioning

confidence: 99%

Voltage Estimation of Active Distribution Network Using PMU Technology

Tangi

Gaonkar

2021

IEEE Access

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As the Distributed Generation (DG) is evolving, the distribution network has undergone tremendous change. The DG sources like wind and solar are intermittent in nature. Hence to estimate the Active distribution network's (ADN) parameters (Voltage and Current) accurately with the DG variation, the traditional offline method like long-term smart distribution network planning (with off-line variant) method is not feasible in terms of network's reliability and operation. In this work, an online voltage estimation technique using PMU (Phasor Measurement Unit) technology is proposed to enhance a distribution network's reliable operation and real-time monitoring. The proposed estimation method does not require PMU units at all nodes in the network. The estimation of unknown states information can be achieved from available PMU unit's data. The proposed methodology is economical and feasible for voltage estimation, and system observability as the number of PMU units required is less. The standard IEEE Distribution network systems are considered for checking the feasibility of the recommended technique. For the simulation of the case studies, MATLAB programming is used. A Forward and Backward sweep (FBS) load-flow algorithm is used to authenticate the proposed methodology.

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Section: A Pmu Placementmentioning

confidence: 99%

Voltage Estimation of Active Distribution Network Using PMU Technology

Tangi

Gaonkar

2021

IEEE Access

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“…

Γ_{n} \in R^{M_{n} \times M_{n}}

is a block diagonal matrix, consisting of 1 + L n blocks. TSA on bus n shifts the phase of the measured quantities by an unknown quantity

normalΔ θ_{n}

, where each block is the 2 × 2 matrix

(][, \begin{matrix} 1em4pt \\ \cos normalΔ θ_{n} & - \sin normalΔ θ_{n} \\ \sin normalΔ θ_{n} & \cos normalΔ θ_{n} \end{matrix})

Once TSDs of one or more substations suffer GPS spoofing‐based TSA and induced time deviation in phasor measurements, it will result in misleading state estimation and in‐appropriate control as a consequence [48, 49]. The potential outcome of TSA has been simulated in [50].…”

Section: Impact Of Tsa On Power Systemsmentioning

confidence: 99%

Review on GPS spoofing‐based time synchronisation attack on power system

Zhang

Peng

Liu

et al. 2020

IET Generation, Transmission & Distribution

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“…The detection process of network-based vulnerability scanning is similar to the ''bottoming'' work before the actual attack by the hacker on the attacking site. The security administrator or network administrator actively implements the security vulnerability scanning to detect and analyze the security threats or security existing in the computer system [27]- [29].…”

Section: Vulnerability Mining Detection Key Technology a Researcmentioning

confidence: 99%

An Intelligent Communication Warning Vulnerability Detection Algorithm Based on IoT Technology

Mao¹,

Xu²,

Xu³

2019

IEEE Access

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This paper mainly studies the vulnerability intelligent early warning technology in the IoT environment, and studies the network security assessment method based on the attack graph association analysis of the IoT environment, and analyzes the attack graph generation algorithm. Firstly, it uses the attack graph technology to establish a network security evaluation model based on the vulnerability association analysis in the IoT environment. The attack graph generation algorithm is improved. The key attack path of the attack graph in the IoT environment is searched according to the node weight value. The key attack path of the network attack graph is used to measure the whole network security, and the security under the IoT environment is given. The measurement calculation model is used to realize the quantitative analysis of the security status of the IoT environment by using the attack graph. Secondly, an intelligent early warning vulnerability detection algorithm based on the dynamic stain propagation model in the IoT environment is proposed, focusing on the introduction of stains and the inspection of stains. A static detection method for early warning vulnerabilities based on the counterexample of the IoT is proposed. Through the flow detection and context sensitive detection, a possible buffer early warning vulnerability is discovered. The driver crawler realizes automatic detection, and uses function hijacking to detect the execution of the stain data. In the experimental environment, compared with the existing tools, the experimental data shows that the algorithm improves the accuracy, recall rate and efficiency of the unfiltered vulnerability of intelligent early warning detection, and proves that the proposed algorithm can effectively detect the vulnerability. INDEX TERMS Security measurement calculation model, IoT, intelligent early warning, vulnerability mining detection.

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Detection of PMU spoofing in power grid based on phasor measurement analysis

Cited by 29 publications

References 20 publications

Voltage Estimation of Active Distribution Network Using PMU Technology

Voltage Estimation of Active Distribution Network Using PMU Technology

Review on GPS spoofing‐based time synchronisation attack on power system

An Intelligent Communication Warning Vulnerability Detection Algorithm Based on IoT Technology

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