Particle Filter Approach to Dynamic State Estimation of Generators in Power Systems

Emami, Kianoush; Fernando, Tyrone; Iu, Herbert Ho-Ching; Trinh, Hieu; Wong, Kit Po

doi:10.1109/tpwrs.2014.2366196

Cited by 100 publications

(26 citation statements)

References 27 publications

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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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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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“…Case study and simulation IEEE 39-bus system, which is also know as the 10-generator New England system, is wildly applied to evaluate the estimation performance of smart grid [8,10]. Fig.…”

Section: Filtering Algorithm In Estimation Centermentioning

confidence: 99%

“…2 describes the constitution of IEEE 39-bus system, where each grid is a generator. The parameters of generators quotes from [10]. The simulation executes 15 seconds after a fault breaks out at the line connecting the bus 14 to bus 15.…”

Section: Filtering Algorithm In Estimation Centermentioning

confidence: 99%

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Sensor Transmission Power Schedule for Smart Grids

Gao

Huang

et al. 2017

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Smart grid has attracted much attention by the requirement of new generation renewable energy. Nowadays, the real-time state estimation, with the help of phasor measurement unit, plays an important role to keep smart grid stable and efficient. However, the limitation of the communication channel is not considered by related work. Considering the familiar limited on-board batteries wireless sensor in smart grid, transmission power schedule is designed in this paper, which minimizes energy consumption with proper EKF filtering performance requirement constrain. Based on the event-triggered estimation theory, the filtering algorithm is also provided to utilize the information contained in the power schedule. Finally, its feasibility and performance is demonstrated using the standard IEEE 39-bus system with phasor measurement units ( PMUs).

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“…The mechanical power is not directly observable and may be obtained using the electrical power measurements and the accelerating power (which is calculated using the speed signal). The choice of UKF is guided by its superior ability to handle nonlinearities efficiently [13], when compared to other parameter estimation techniques used with power system measurements, such as least square [14], extended Kalman filter (EKF) [15], particle filter [16], trajectory sensitivity [17].…”

mentioning

confidence: 99%

Estimation of Inherent Governor Dead-Band and Regulation Using Unscented Kalman Filter

Bhui

Senroy

Singh

et al. 2018

IEEE Trans. Power Syst.

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Abstract-The inclusion of the governor droop and dead-band in dynamic models helps to reproduce the measured frequency response accurately and is a key aspect of model validation. Often, accurate and detailed turbine-governor information are not available for various units in an area control centre. The uncertainty in the droop also arise from the nonlinearity due to the governor valves. The droop and deadband are required to tune the secondary frequency bias factors, and to determine the primary frequency reserve. Earlier research on droop estimation did not adequately take into account the effect of dead-band and other nonlinearities. In this paper, unscented Kalman filter is used in conjunction with continuously available measurements to estimate the governor droop and the dead-band width. The effectiveness of the approach is demonstrated through simulations.

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Particle Filter Approach to Dynamic State Estimation of Generators in Power Systems

Cited by 100 publications

References 27 publications

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

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

Sensor Transmission Power Schedule for Smart Grids

Estimation of Inherent Governor Dead-Band and Regulation Using Unscented Kalman Filter

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