Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter

Nan, Dongliang; Wang, Weiqing; Wang, Kaike; Mahfoud, Rabea Jamil; Alhelou, Hassan Haes; Siano, Pierluigi

doi:10.3390/app9235200

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Meanwhile, the UKF and CKF exhibit almost the same performance. Recently, the Kalman filter has been developed in practical systems, such as the distributed Kalman filter [ 37 ] for sensor networks, hybrid Kalman filter [ 38 ], and adaptive ensemble square root Kalman filter [ 39 ].…”

Section: State Estimation Based On a Distinct Modelmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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Section: State Estimation Based On a Distinct Modelmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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show abstract

“…In [2], an adaptive ensemble square root Kalman filter (AEnSRF) has been proposed, in which the ensemble square root filter (EnSRF) and Sage-Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method showed that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.…”

Section: Published Papersmentioning

confidence: 99%

Special Issue on Advances and Technologies in High Voltage Power Systems Operation, Control, Protection, and Security

Alhelou

Siano

2020

Applied Sciences

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“…Since the CKF often encounters the problem of filter divergence and the loss of positive definiteness caused by the computational errors of arithmetic operations in practical applications [16], the SCKF, originally proposed by Ienkaram and Haykin, propagates the square root of the error covariance matrix to improve stability and accuracy [2], [18], [30]. As a result, the SCKF, as an improvement to the CKF, has shown promise in nonlinear systems and has been used for state estimation with unknown noise.…”

Section: The Square-root Cubature Kalman Filtermentioning

confidence: 99%

Adaptive Cubature Kalman Filter Based on the Expectation-Maximization Algorithm

Zhou

2019

IEEE Access

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A cubature Kalman filter is considered to be one of the most useful methods for nonlinear systems. However, when the statistical characteristics of noise are unknown, the estimation accuracy is degraded. Therefore, an adaptive square-root cubature Kalman filter (ASCKF) is designed to handle the unknown noise. The maximum likelihood criterion and expectation-maximization algorithm are employed to adaptively estimate the parameters of unknown noise, thus restraining the disturbance resulting from unknown noise and improving the estimation accuracy. The stability of the proposed algorithm is theoretically analyzed. Finally, simulations are carried out to illustrate that the performance of the ASCKF algorithm is much more reliable than that of a standard square-root cubature Kalman filter.

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Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter

Cited by 7 publications

References 26 publications

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Special Issue on Advances and Technologies in High Voltage Power Systems Operation, Control, Protection, and Security

Adaptive Cubature Kalman Filter Based on the Expectation-Maximization Algorithm

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