Advanced Metering for Phase Identification, Transformer Identification, and Secondary Modeling

Short, T.A.

doi:10.1109/tsg.2012.2219081

Cited by 182 publications

(67 citation statements)

References 4 publications

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“…It can isolate an intruder with less overhead, and it can track the intruder to continually prevent the attack. Papers Wang and Yi (2011), Yi et al (2012, 2013, and Zhu et al (2011) discuss some security mechanisms, and this paper discusses a DoS attack and defense in Advanced Metering Infrastructure Network.…”

Section: Related Workmentioning

confidence: 99%

“…The role of an AMI is to enable communication between utility companies and electricity meters, including remote electricity usage readings (on-demand and periodic), sending of updated price information to the meters, transmission of alerts about outages, and upgrades of meter firmware, among other communications. Some messages require real-time delivery, while others can be buffered and delayed without negative consequences (Short, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Puppet attack: A denial of service attack in advanced metering infrastructure network

Zhu

Zhang

et al. 2016

Journal of Network and Computer Applications

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a b s t r a c tAdvanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI shares information about consumption, outages, and electricity rates reliably and efficiently by bidirectional communication between smart meters and utilities. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities assets or steal customers private information.In this paper, we present a new DoS attack, called puppet attack, which can result in denial of service in AMI network. The intruder can select any normal node as a puppet node and send attack packets to this puppet node. When the puppet node receives these attack packets, this node will be controlled by the attacker and flood more packets so as to exhaust the network communication bandwidth and node energy. Simulation results show that puppet attack is a serious and packet deliver rate goes down to 20-10%. After analyzing the puppet attack, we propose the detection and prevention mechanism. Simulations show that puppet attack causes the same damage as a flooding attack and the proposed method can prevent the puppet attack efficiently.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Puppet attack: A denial of service attack in advanced metering infrastructure network

Zhu

Zhang

et al. 2016

Journal of Network and Computer Applications

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“…Topology error detection regarding smart meter placement in a GIS system is introduced in [23]. Short [24] presented a method for meter phase identification and meter-to-transformer mapping by applying a voltage drop equation and linear regression that utilize AMI energy and voltage measurements. In this paper, the voltage drop equation and regression analysis are applied to estimate low-voltage circuit impedances in the Georgia Tech campus distribution system.…”

Section: A Backgroundmentioning

confidence: 99%

Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness

Peppanen

Reno

Thakkar

et al. 2015

IEEE Trans. Smart Grid

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The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. We demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.

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“…In this approach, Ref. [8], [9] use voltage magnitude profiles for the correlation analysis. In [10], salient features are extracted from load profiles for the correlation analysis.…”

Section: Introductionmentioning

confidence: 99%

Maximum Marginal Likelihood Estimation of Phase Connections in Power Distribution Systems

Wang

2020

IEEE Trans. Power Syst.

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Accurate phase connectivity information is essential for advanced monitoring and control applications in power distribution systems. The existing data-driven approaches for phase identification lack precise physical interpretation and theoretical performance guarantee. Their performance generally deteriorates as the complexity of the network, the number of phase connections, and the level of load balance increase. In this paper, by linearizing the three-phase power flow manifold, we develop a physical model, which links the phase connections to the smart meter measurements. The phase identification problem is first formulated as a maximum likelihood estimation problem and then reformulated as a maximum marginal likelihood estimation problem. We prove that the correct phase connection achieves the highest log likelihood values for both problems. An efficient solution method is proposed by decomposing the original problem into subproblems with a binary least-squares formulation. The numerical tests on a comprehensive set of distribution circuits show that our proposed method yields very high accuracy on both radial and meshed distribution circuits with a combination of single-phase, two-phase, and three-phase loads. The proposed algorithm is robust with respect to inaccurate feeder models and incomplete measurements. It also outperforms the existing methods on complex circuits.

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Advanced Metering for Phase Identification, Transformer Identification, and Secondary Modeling

Cited by 182 publications

References 4 publications

Puppet attack: A denial of service attack in advanced metering infrastructure network

Puppet attack: A denial of service attack in advanced metering infrastructure network

Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness

Maximum Marginal Likelihood Estimation of Phase Connections in Power Distribution Systems

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