Dynamic state estimation in power systems using Kalman filters

Tebianian, Hamed; Jeyasurya, B.

doi:10.1109/epec.2013.6802979

Cited by 17 publications

(7 citation statements)

References 6 publications

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“…A server shown in the figure is used to gather data for activity tracking and display purposes. Kalman filter based SE using PMU data [42] is used as the critical algorithm to be secured. Additionally, the scalability of the proposed framework is tested using simulations on the IEEE 118 bus system.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

A Distributed Hierarchy Framework for Enhancing Cyber Security of Control Center Applications

Kuraganti,

Robert,

Gurrala

et al. 2020

Preprint

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Recent cyber-attacks on power grids highlight the necessity to protect the critical functionalities of a control center vital for the safe operation of a grid. Even in a distributed framework one central control center acts as a coordinator in majority of the control center architectures. Such a control center can become a prime target for cyber as well as physical attacks, and, hence, a single point failure can lead to complete loss of visibility of the power grid. If the control center which runs the critical functions in a distributed computing environment can be randomly chosen between the available control centers in a secure framework, the ability of the attacker in causing a single point failure can be reduced to a great extent. To achieve this, a novel distributed hierarchy based framework to secure critical functions is proposed in this paper. The proposed framework ensures that the data aggregation and the critical functions are carried out at a random location, and incorporates security features such as attestation and trust management to detect compromised agents. A theoretical result is proved on the evolution and convergence of the trust values in the proposed trust management protocol. It is also shown that the system is nominally robust so long as the number of compromised nodes is strictly less than one-half of the nodes minus 1. For demonstration, a Kalman filter-based state estimation using phasor measurements is used as the critical function to be secured. The proposed framework's implementation feasibility is tested on a physical hardware cluster of Parallella boards. The framework is also validated using simulations on the IEEE 118 bus system.

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Section: Experimental Setup and Resultsmentioning

confidence: 99%

A Distributed Hierarchy Framework for Enhancing Cyber Security of Control Center Applications

Kuraganti,

Robert,

Gurrala

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Both of the aforementioned filters take different approaches to accomplishing this task. Both EKF and UKF formulations can be found in [27], and the two final stages of DSE in this paper, i.e., forecasting and filtering, are based on these. Figure 1 depicts the DSE procedure with the addition of EKF and UKF.…”

Section: Dse and The Generator Modelmentioning

confidence: 99%

“…Ref. [27] uses novel Kalman filters to perform DSE. Reference [28] introduces hierarchical clustering applications for anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Framework for Detecting and Eliminating Cyber-Attacks in Power Grids

et al. 2021

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The work described in this paper aims to detect and eliminate cyber-attacks in smart grids that disrupt the process of dynamic state estimation. This work makes use of an unsupervised learning method, called hierarchical clustering, in an attempt to create an artificial sensor to detect two different cyber-sabotage cases, known as false data injection and denial-of-service, during the dynamic behavior of the power system. The detection process is conducted by using an unsupervised learning-enhanced approach, and a decision tree regressor is then employed for removing the threat. The dynamic state estimation of the power system is done by Kalman filters, which provide benefits in terms of the speed and accuracy of the process. Measurement devices in utilities and buses are vulnerable to communication interruptions between phasor measurement units and operators, who can be easily manipulated by false data. While Kalman filters are incapable of detecting the majority of such cyber-attacks, this article proves that the proposed unsupervised machine learning method is able to detect more than 90 percent of the mentioned attacks. The simulation results on the IEEE 9-bus with 3-machines and IEEE 14-bus with 5-machines systems verify the efficiency of the proposed approach.

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“…A hybrid algorithm for solving the SE problem by using weighted least square, weighted least absolute value and particle swarm optimization (PSO) methods is presented in [4]. In [5] uses unscented Kalman filter and extended Kalman filter to estimate power system states by a phasor measurement unit (PMU). A comparative analysis of SE in rectangular and polar coordinates has been proposed in [6].…”

Section: Introductionmentioning

confidence: 99%

Power system state estimation using teaching learning-based optimization algorithm

Salkuti

2020

TELKOMNIKA

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The main goal of this paper is to formulate power system state estimation (SE) problem as a constrained nonlinear programming problem with various constraints and boundary limits on the state variables. SE forms the heart of entire real time control of any power system. In real time environment, the state estimator consists of various modules like observability analysis, network topology processing, SE and bad data processing. The SE problem formulated in this work is solved using teaching leaning-based optimization (TLBO) technique. Difference between the proposed TLBO and the conventional optimization algorithms is that TLBO gives global optimum solution for the present problem. To show the suitability of TLBO for solving SE problem, IEEE 14 bus test system has been selected in this work. The results obtained with TLBO are also compared with conventional weighted least square (WLS) technique and evolutionary based particle swarm optimization (PSO) technique.

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Dynamic state estimation in power systems using Kalman filters

Cited by 17 publications

References 6 publications

A Distributed Hierarchy Framework for Enhancing Cyber Security of Control Center Applications

A Distributed Hierarchy Framework for Enhancing Cyber Security of Control Center Applications

A Hybrid Framework for Detecting and Eliminating Cyber-Attacks in Power Grids

Power system state estimation using teaching learning-based optimization algorithm

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