Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability

Qi, Junjian; Sun, Kai; Wang, Jianhui; Liu, Hui

doi:10.1109/tsg.2016.2580584

Cited by 163 publications

(76 citation statements)

References 34 publications

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“…• SR-UKF is an improved variant and more numerically stable than UKF [14]. The SR-UKF is set to use the same constants as those on UKF to generate sigma points.…”

Section: Comparing L ∞ Observer With Other Estimation Methodsmentioning

confidence: 99%

“…For example, extended Kalman filter (EKF) is applied to perform DSE [7], [8], which works only in a mild nonlinear environment and when Jacobian matrix exists. As a derivativefree alternative, unscented Kalman filter (UKF) does not require linearization or calculation of Jacobian matrices [9]- [14]. Using spherical-radial cubature rule, Arasaratnam et al propose cubature Kalman filter (CKF) [15], which has been shown to have improved performance compared with EKF and UKF [16].…”

Section: Introductionmentioning

confidence: 99%

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

Nugroho

Taha

2020

IEEE Trans. Power Syst.

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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.

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“…• SR-UKF is an improved variant and more numerically stable than UKF [14]. The SR-UKF is set to use the same constants as those on UKF to generate sigma points.…”

Section: Comparing L ∞ Observer With Other Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Nugroho

Taha

2020

IEEE Trans. Power Syst.

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“…It is noticed that the matricesR andQ in (19) and (26) may become negative definite during the process of the implementation (this is also mentioned in [29]). In this work we calculate the nearest positive definite matrices ofR orQ when negative eigenvalues ofR orQ are observed, such that a symmetric positive definite matrix nearest toR orQ in terms of the Frobenius norm can be obtained [32].…”

Section: Parameter Estimationmentioning

confidence: 99%

AutoRally: An Open Platform for Aggressive Autonomous Driving

et al. 2019

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Scaled platforms constructed from modified RC cars are popular in the academic and hobby communities. These platforms are typically 0.2 m to 1 m long and weigh between 1 kg and 25 kg. Costs range from a few hundred to tens of thousands of dollars, largely determined by the size, sensors, and computing. Construction, maintenance, and programming is typically handled by a small team of students or researchers. Recently, several open source projects released complete documentation and interface software, which is in contrast to the one-off nature of older work that often lacked enough information to replicate. Documentation for open source platforms normally includes parts lists, build instructions, 3 and interface software for the sensors and actuators. Availability of tutorials, simulation environments, and public datasets vary by project. Common sensors include wheel speed, inertial measurement unit (IMU), cameras, depth sensors, ultrasonic, and light detection and ranging (Lidar) units. The target environment for these platforms is typically indoors on a smooth surface. The Donkey Car [5] is an easy to build 1:16 scale autonomous platform for the DIY Roborace events targeted at hobbyists. Onboard computing and sensing are a Raspberry Pi 3 with a matching wide angle camera. The Berkeley Autonomous Race Car (BARC) [6] is a 1:10 scale vehicle designed as a simple and affordable research platform for self-driving vehicle technologies that has been successfully used to demonstrate various control algorithms. The onboard ODROID-XU4 is similar in computational performance to the Raspberry Pi 3, and the sensor suite includes a hobby grade camera, IMU, four ultrasonic range finders, and Hall effect wheel speed sensors. The F1/10 project [7] and accompanying Autonomous Racing Competition allows teams to race against one another using a common 1:10 scale platform developed at the University of Pennsylvania. Computing on the F1/10 platform is performed by an Nvidia Jetson. The sensor suite includes a hobby IMU, compact indoor Hokuyo 2D Lidar, and optional Structure and Zed depth and motion sensing cameras. The 1:10 scale Rapid Autonomous Complex-Environment Competing Ackermann-steering Robot (RACECAR) [8] from Massachusetts Institute of Technology is a platform for researchers creating applications for self driving cars. RACECAR also uses the Nvidia Jetson for computing, and includes the same Hokuyo Lidar and Zed stereo camera as the F1/10 platform. Table 1 provides a comparison of these open source scaled platforms.

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“…In recent years, the studies on state estimators began to focus on a synchronous generator and its electromechanical transient model [8][9][10].In essence,this is a typical nonlinear filter problem. Up to now, there has been a significantly amount of studies on DSE of synchronous machines by using particle filters (PF) [11,12] and variousKalman-type filtering algorithms, such asextended Kalman filter (EKF) [13][14][15][16][17], unscented Kalman filter (UKF) [18][19][20][21][22][23][24], and Cubature Kalman Filter (CKF) [3,25,26].The EKF is a classical nonlinear Kalman filter; the unscented transform-basedUKFprovidesreasonable filtering performance, but its convergence is dependent on the sampling methods of Sigma points [18,19]; the CKF based on the spherical-radial cubature rule is an emerging nonlinear filter, which can give a systematic solution for highdimensional nonlinear filtering issues.Extensive comparisons of all these Kalman-type estimators have been made from different perspectives, such as convergence, numerical stability, and computational complexity in [3,16].…”

Section: B Literature Reviewmentioning

confidence: 99%

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

et al. 2019

IEEE Access

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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.

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Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability

Abstract: Abstract-In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance

Cited by 163 publications

References 34 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

AutoRally: An Open Platform for Aggressive Autonomous Driving

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

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