Topology processing and static state estimation using artificial neural networks

Kumar, D. M. Vinod; Srivastava, S. C.; Shah, Sapan; Mathur, SK

doi:10.1049/ip-gtd:19960050

Cited by 39 publications

(1 citation statement)

References 14 publications

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“…There are numerous methods proposed to solve it in the literature. AI‐based algorithms are typically based on neural networks, and they involve a system that learns and improves estimation based on successive measurements [25]. Singular value decomposition (SVD) is a method that has seen many applications in linear algebra among others data compression and solving linear systems [26].…”

Section: Solving the Parameter Estimation Equationmentioning

confidence: 99%

Topology and parameter estimation in power systems through inverter‐based broadband stimulations

Neshvad

Margossian

Sachau

2016

IET Generation, Transmission & Distribution

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Section: Solving the Parameter Estimation Equationmentioning

confidence: 99%

Topology and parameter estimation in power systems through inverter‐based broadband stimulations

Neshvad

Margossian

Sachau

2016

IET Generation, Transmission & Distribution

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Identification of topology changes in power grids using phasor measurements

Wiel

Bent

Casleton

et al. 2014

Appl Stoch Models Bus & Ind

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Phasor measurement units (PMUs) are increasingly important for monitoring the state of an electrical power grid and quickly detecting topology changes caused by events such as lines going down or large loads being dropped. Phasors are complex valued measurements of voltage and current at various points of generation and consumption. If a line goes down or a load is removed, power flows change throughout the grid according to known physical laws and the probability distribution of phasor measurements changes accordingly. This paper develops a method to estimate the current topology of a power grid from phasor measurements and considers the design goal of placing PMUs at strategic points in a distribution system to achieve good sensitivity to single-line outages. From a vector of phasor measurements probabilities are computed corresponding to the scenario that all power lines are operational and to alternate scenarios in which each line goes down individually. These probabilities are functions of the joint distributions of phasor measurements under each possible scenario, obtained through Monte Carlo simulations with random load profiles. We use log-spline densities to estimate marginal distributions of phasor measurements and fold these into a multivariate Gaussian copula to capture important correlations. Sensitivity to outages varies according to which line goes down and where PMUs are placed on the grid. A greedy search algorithm is demonstrated for placing PMUs at locations that provide good sensitivity to single-line outages.

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Adversarial False Data Injection Attack Against Nonlinear AC State Estimation with ANN in Smart Grid

Tian

Shu

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Artificial neural network (ANN) provides superior accuracy for nonlinear alternating current (AC) state estimation (SE) in smart grid over traditional methods. However, research has discovered that ANN could be easily fooled by adversarial examples. In this paper, we initiate a new study of adversarial false data injection (FDI) attack against AC SE with ANN: by injecting a deliberate attack vector into measurements, the attacker can degrade the accuracy of ANN SE while remaining undetected. We propose a population-based algorithm and a gradientbased algorithm to generate attack vectors. The performance of these algorithms are evaluated through simulations on IEEE 9-bus, 14-bus and 30-bus systems under various attack scenarios. Simulation results show that DE is more effective than SLSQP on all simulation cases. The attack examples generated by DE algorithm successfully degrade the ANN SE accuracy with high probability.

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Topology processing and static state estimation using artificial neural networks

Cited by 39 publications

References 14 publications

Topology and parameter estimation in power systems through inverter‐based broadband stimulations

Topology and parameter estimation in power systems through inverter‐based broadband stimulations

Identification of topology changes in power grids using phasor measurements

Adversarial False Data Injection Attack Against Nonlinear AC State Estimation with ANN in Smart Grid

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