Robust adaptive Unscented Kalman Filter with gross error detection and identification for power system forecasting-aided state estimation

Zhang, Zhenhui; Zhang, Zhengjiang; Zhao, S. J.; Li, Quanfang; Hong, Zhihui; Li, Fuhua; Huang, Shipei

doi:10.1016/j.jfranklin.2023.07.022

Cited by 7 publications

(11 citation statements)

References 35 publications

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“…Step 1: For the state prediction error, in the standard UKF algorithm, the weighted state prediction value is used instead of the true value, and an approximate error is inevitably introduced in this process. The approximate error is considered in the real value, and the first-order information in the approximate error is replaced by ξ (1) (k) in the following equation.…”

Section: Unscented Kalman Filtering Algorithm Considering High-order ...mentioning

confidence: 99%

“…Remark 2. The superscript 1 in x (1) (k + 1)is used to indicate that the first-order approximate error information is introduced into the state vector, and the subsequent superscript numbers all represent the approximate error information of the corresponding order. x (l) (k + 1) differs from x(k + 1) in that the lth-order information of the approximate error is already taken into account in the state vector of the former.…”

Section: Unscented Kalman Filtering Algorithm Considering High-order ...mentioning

confidence: 99%

“…The state estimation is usually implemented by a filter, which uses real-time measurements and dynamic models of the system to improve the data accuracy and obtain the estimated state of the system [1]. In 1960, Kalman proposed a filtering method for linear systems that was soon widely used [2].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Extended Unscented Kalman Filter Is Designed Using the Higher-Order Statistical Property of the Approximate Error of the System Model

Li,

Wen

2024

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In the actual working environment, most equipment models present nonlinear characteristics. For nonlinear system filtering, filtering methods such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Cubature Kalman Filter (CKF) have been developed successively, all of which show good results. However, in the process of nonlinear system filtering, the performance of EKF decreases with an increase in the truncation error and even diverges. With improvement of the system dimension, the sampling points of UKF are relatively few and unrepresentative. In this paper, a novel high-order extended Unscented Kalman Filter (HUKF) based on an Unscented Kalman Filter is designed using the higher-order statistical properties of the approximate error. In addition, a method for calculating the approximate error of the multi-level approximation of the original function under the condition that the measurement is not rank-satisfied is proposed. The effectiveness of the filter is verified using digital simulation experiments.

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Section: Unscented Kalman Filtering Algorithm Considering High-order ...mentioning

confidence: 99%

Section: Unscented Kalman Filtering Algorithm Considering High-order ...mentioning

confidence: 99%

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A Novel Extended Unscented Kalman Filter Is Designed Using the Higher-Order Statistical Property of the Approximate Error of the System Model

Li,

Wen

2024

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“…A robust UKF algorithm based on generalized maximum mixture correntropy criterion is proposed for FASE in [26] which showed better accuracy and robustness compared to traditional correntropy algorithms. In the further study, to incorporate the influence of gross errors on SE, an adaptive UKF method is adopted in [27]. It estimates the gross errors in measurements to compensate during the intermediate step, so these compensated measurements are used for the corrected state estimation step.…”

Section: Introductionmentioning

confidence: 99%

Forecasting-Aided State Estimation in Power Systems During Normal Load Variations Using Iterated Square-Root Cubature Kalman Filter

Johnson,

Banu,

Moger

2024

J. Electron. Electric. Eng.

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The main aim is to estimating the voltage profile at all the buses in the system before the arrival of next set of hybrid measurements from field. The effectiveness of the algorithm ISCKF during load variations is evaluated with respect to already implemented Kalman filter approaches for this application. This includes the sudden changes in loads which occurring in practical power systems. The utilization of an iterated square-root cubature Kalman filter (ISCKF) for power system forecasting-aided state estimation (FASE) is being studied during normal load variations. Its implementation involves "Newton-Gauss iterative method being embedded into the square-root cubature Kalman filter (SCKF)" at the measurement update step of Kalman filter. The square-root factor of error covariance matrices is calculated by utilizing QR decomposition to avoid losing of positive definiteness of the matrix. The estimation is carried out utilizing hybrid measurements from remote terminal units and phasor measurement units. The state vector is forecasted using the proposed method in the interval period between two measurement arrivals from the devices. Thereby, caters to state estimation of the voltages at buses in the system even when the measurements are unavailable. The efficacy of the proposed algorithm to FASE is evaluated for IEEE 30-bus system and Northern Region Power Grid (NRPG) 246-bus system. The simulation results show that the proposed ISCKF outperforms the CKF by significant improvement in accuracy of forecasting-aided state estimation. ISCKF will be able to give results before the next set of hybrid data arrives (expected from an estimation algorithm). Therefore, the proposed estimation algorithm is applicable for real-time practical application, with respect to large power systems as well.

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“…Therefore, UKF can only be applied to simple industrial processes with known models. When applying UKF to a system with an unknown model, there may be obstacles that can sometimes be achieved by simplifying the model, but this is not a good solution [13]. Through data-driven methods, models can be trained using only the input and output data of the system, without knowing how complex the internal processes are.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear auto regressive Elman neural network combined with unscented Kalman filter for data-driven dynamic data reconciliation in dynamic systems

Zhang,

Hong,

Zhang

et al. 2023

Meas. Sci. Technol.

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Measurement data plays an important role in the control system, but the data collected by sensors often has measurement noise, which makes the state of the system cannot be accurately revealed. Unscented Kalman filter (UKF) is a highly accurate and robust filtering algorithm, but its limitation is the requirement of prior knowledge of the exact dynamic mathematical model, which is a critical issue to be addressed in practice. In this work, a data-driven dynamic data reconciliation scheme called nonlinear auto regressive Elman neural network (ENN) with exogenous inputs combined with UKF (NARX-ENN-UKF) is proposed, where nonlinear auto regressive ENN with exogenous inputs is used for dynamic data-driven modeling, and then UKF is applied for dynamic data reconciliation of the measurements based on the trained model. The scheme is applied to a DC/AC inverter experimental system and a self-developed sliding electrical contact experimental system to verify the effectiveness of NARX-ENN-UKF.

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Robust adaptive Unscented Kalman Filter with gross error detection and identification for power system forecasting-aided state estimation

Cited by 7 publications

References 35 publications

A Novel Extended Unscented Kalman Filter Is Designed Using the Higher-Order Statistical Property of the Approximate Error of the System Model

A Novel Extended Unscented Kalman Filter Is Designed Using the Higher-Order Statistical Property of the Approximate Error of the System Model

Forecasting-Aided State Estimation in Power Systems During Normal Load Variations Using Iterated Square-Root Cubature Kalman Filter

Nonlinear auto regressive Elman neural network combined with unscented Kalman filter for data-driven dynamic data reconciliation in dynamic systems

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