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

Li, Yang; Li, Jing; Qi, Junjian; Chen, Liang

doi:10.1109/access.2019.2900228

Cited by 77 publications

(56 citation statements)

References 40 publications

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“…Generally, in the process of estimating the state of power systems, the system equations and the measurement equations are concentrated in the following equation [1][2][3]22, 27, 31-34]…”

Section: Generator Modelmentioning

confidence: 99%

“…where P is the equivalence weight matrix. In this work, the Huber method is used to calculate the equivalence weight matrix P [1], which is given by x  obtained in the forecasting stage remains unchanged. In this regard, this paper proposes an attack identification strategy, which is as follows:…”

Section: B Robust Cubature Kalman Filtermentioning

confidence: 99%

“…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%

“…x and it x are respectively the estimated value and the true value, iz x represents the measurement value, N denotes the number of sampling points. The evaluation index 1  can measure the filtering performance of the same state estimation method under different cyber attacks; 2  can evaluate the filtering performances of different state estimation methods under the same cyber attack; 3  can quantitatively evaluate the estimation results for any estimation method under any attack.…”

Section: B Evaluation Indexmentioning

confidence: 99%

“…m  is the mean square error of r m .C is a given constant (ranging from 1.3 to 2.0), and it is here taken as 1.5 through the try-and-error method. These vectors are expressed as[1]:  is the measurement error variance matrix before correction.In the process of the RCKF-based DSE of generators, 1kkx  and 11kkx  are approximately equal before attacks. When attack vectors are implemented into the measurement vectors, 11kkx  obtained in the filtering stage will change.…”

mentioning

confidence: 99%

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Dynamic State Estimation of Generators Under Cyber Attacks

Yang

Liang

2019

IEEE Access

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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

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“…Generally, in the process of estimating the state of power systems, the system equations and the measurement equations are concentrated in the following equation [1][2][3]22, 27, 31-34]…”

Section: Generator Modelmentioning

confidence: 99%

Section: B Robust Cubature Kalman Filtermentioning

confidence: 99%

Section: All Simulations Have Been Performed Under the Matlab Environmentioning

confidence: 99%

Section: B Evaluation Indexmentioning

confidence: 99%

mentioning

confidence: 99%

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Dynamic State Estimation of Generators Under Cyber Attacks

Yang

Liang

2019

IEEE Access

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Engineering approach to construct robust filter for mismatched nonlinear dynamic systems

Emami,

Araújo,

Cruz

et al. 2024

Intl J Robust & Nonlinear

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This article proposes a novel approach to design a robust estimator that is able to keep its consistency in system state estimation when system process model mismatch occurs. To successfully develop such an estimator, not only the estimation strategy proposed but also the designer's knowledge and experience about the system behavior are crucial and determining. To assess the performance of the resultant estimator, its performance is compared with that of three well‐known estimators, that is, the unscented Kalman filter, the cubature Kalman filter, and the extended Kalman filter on the IEEE 5‐generator 14‐bus system. The results indicate that the proposed method has led to an estimator outperforming its rivals under the presence of model errors.

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