Comparing Kalman Filters and Observers for Power System Dynamic State Estimation With Model Uncertainty and Malicious Cyber Attacks

Qi, Junjian; Taha, Ahmad F.; Wang, Jianhui

doi:10.1109/access.2018.2876883

Cited by 82 publications

(60 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The constants to generate sigma points are set to be α = 0.1, β = 2, and κ = −1. The initial state error covariance matrix is the same as in (16).…”

Section: Comparing L ∞ Observer With Other Estimation Methodsmentioning

confidence: 99%

“…The same test system has been used in [16]. The input vector u(t), which consists of i R and i I , and unknown input q m (t), which consists of T m and E fd , are obtained from simulations of the whole system in which each generator is using a transient model with IEEE Type DC1 excitation system and a simplified turbinegovernor system [16], [51]. To generate dynamic response, we apply a three-phase fault at Bus 32 of Branch 32-30 and clear the fault at the near and remote ends after 0.05 and 0.1 seconds.…”

Section: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees

Nugroho

Taha

2020

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

A robust observer for performing power system dynamic state estimation (DSE) of a synchronous generator is proposed. The observer is developed using the concept of L∞ stability for uncertain, nonlinear dynamic generator models. We use this concept to (i) design a simple, scalable, and robust dynamic state estimator and (ii) obtain a performance guarantee on the state estimation error norm relative to the magnitude of uncertainty from unknown generator inputs, and process and measurement noises. Theoretical methods to obtain upper and lower bounds on the estimation error are also provided. Numerical tests validate the performance of the L∞-based estimator in performing DSE under various scenarios. The case studies reveal that the derived theoretical bounds are valid for a variety of case studies and operating conditions, while yielding better performance than existing power system DSE methods.

show abstract

“…The constants to generate sigma points are set to be α = 0.1, β = 2, and κ = −1. The initial state error covariance matrix is the same as in (16).…”

Section: Comparing L ∞ Observer With Other Estimation Methodsmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees

Nugroho

Taha

2020

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Accurate and reliable dynamic state information of synchronous machines plays a crucial role in real-time monitoring, protection, and control of power systems [1,2]. In addition, the emerging application of situational awarenessputs forward higher requirements for the status information acquisition as the system states evolve more complexly and quickly due to increasing cyber attacks [3] and high penetrations of renewable generations [4,5]. However, some important synchronous machine state variables cannot be directly obtained.…”

Section: A Motivationmentioning

confidence: 99%

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theorywith the classical CKF,which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviatefrom the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.INDEX TERMSDynamic state estimation, cubature Kalman filter, synchronous machines,M-estimation theory,unknown noise statistics, non-Gaussian noise, outliers, PMU data.

show abstract

“…The simulation data are listed as follows: 1) The sampling rate is assumed 50 samples/s; 2) The simulation time step is set to 0.02 s; 3) The standard deviations of the rotor angle and the rotor speed are respectively 2° and 0.1%; 4) The standard deviations of the phase angle and amplitude of terminal voltages are 0.1° and 0.1%；5) A PMU is equipped at the terminal of each generator [1,27].…”

Section: All Simulations Have Been Performed Under the Matlab Environmentioning

confidence: 99%

Dynamic State Estimation of Generators Under Cyber Attacks

Yang

Liang

2019

IEEE Access

View full text Add to dashboard Cite

Accurate and reliable estimation of generator's dynamic state vectors in real time are critical to the monitoring and control of power systems. A robust Cubature Kalman Filter (RCKF) based approach is proposed for dynamic state estimation (DSE) of generators under cyber attacks in this paper. First, two types of cyber attacks, namely false data injection and denial of service attacks, are modelled and thereby introduced into DSE of a generator by mixing the attack vectors with the measurement data; Second, under cyber attacks with different degrees of sophistication, the RCKF algorithm and the Cubature Kalman Filter (CKF) algorithm are adopted to the DSE, and then the two algorithms are compared and discussed. The novelty of this study lies primarily in our attempt to introduce cyber attacks into DSE of generators. The simulation results on the IEEE 9-bus system and the New England 16-machine 68-bus system verify the effectiveness and superiority of the RCKF. INDEX TERMS Dynamic state estimation, cyber attacks, false data injection, denial of service, generator, robust cubature Kalman filter, PMU data

show abstract

Comparing Kalman Filters and Observers for Power System Dynamic State Estimation With Model Uncertainty and Malicious Cyber Attacks

Cited by 82 publications

References 50 publications

Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees

Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

Dynamic State Estimation of Generators Under Cyber Attacks

Contact Info

Product

Resources

About