Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-8· 0 citations· 10 references
Abstract
Dynamic state estimation (DSE) plays a critical role in the monitoring and control of modern electrical power systems, particularly under time-varying operating conditions. Conventional estimators such as the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) rely on nonlinear dynamic models in the prediction stage, which may introduce computational complexity and numerical sensitivity. This paper proposes a decoupled Koopman-based tracking framework in which a data-driven Koopman operator is employed exclusively as a linear prediction mechanism prior to the estimation stage, while preserving the classical correction structure of conventional estimators. The approach is validated on the IEEE 14-bus system under dynamic load variations over 300 operating points. Performance is evaluated in terms of global and nodal root mean square error (RMSE), measurement residual metrics, execution time statistics, and operational robustness under latency constraints. The results show that the Koopman-enhanced estimators maintain competitive estimation accuracy while improving measurement fitting consistency and computational stability. Furthermore, the proposed tracking mechanism demonstrates bounded execution time variability and enhanced robustness in real-time scenarios. The findings confirm that Koopmanbased tracking provides a viable and computationally efficient enhancement for dynamic state estimation in power systems.
This paper proposes a computationally efficient dynamic state estimation algorithm for nonlinear power networks using synchronized phasor measurements and a combination of an artificial neural network and a linear Kalman filter. The methodology involves using both the predicting abilities of the artificial neural network (ANN) and the iterative correction properties of the linear Kalman filter to estimate system states under dynamic operating conditions. Instead of using the nonlinear estimation approaches requiring the repeated computation of Jacobians and matrices, the presented approach significantly reduces the computational complexity while maintaining accurate estimation. The ANN is first trained on PMU measurement data and generates predictive state values for the system, which are corrected using the linear Kalman filter to remove the effect of noise and increase resistance to measurement corruption and malicious attacks. Numerical simulation experiments carried out on the IEEE 6-bus test network have shown that the hybrid state estimator provides more accurate results in terms of reduced RMSE and faster convergence when compared to the conventional state estimation methods.
Suvraujjal Dutta, A. Shrivastav, Sandip Chanda et al.· Scientific Reports· 0 citations
An Adaptive-Parameter Maximum Correntropy Kalman Filter (APMCKF) algorithm is proposed to address state estimation degradation in semi-active suspensions caused by non-linear coupling between time-varying physical parameters and non-Gaussian noise. First, a time-varying dynamic model with non-linear damping is established via bench tests. A genetic algorithm (GA) globally optimizes key physical parameters to suppress model mismatch. Second, the APMCKF integrates an adaptive suspension parameter update mechanism. This closed-loop mechanism refreshes the system state matrix in real-time, effectively overcoming state-tracking lag. Concurrently, the maximum correntropy criterion (MCC) is embedded within the Sage–Husa recursive framework to dynamically reconstruct the observation noise covariance matrix, ensuring robust filtering under heavy-tailed noise. Simulations under ISO Class A–D random road profiles demonstrate that the APMCKF reduces the root-mean-square error (RMSE) by 62.33–81.24% compared to the adaptive Kalman filter (AKF). It also outperforms the adaptive-parameter Kalman filter (APKF), yielding a 27.49% accuracy improvement on Class D roads where non-Gaussian noise is most severe. Moreover, comparative evaluations against standard non-linear Bayesian filters demonstrate that the APMCKF successfully overcomes the truncation errors of the Extended Kalman Filter (EKF) and the tracking hysteresis of the Unscented Kalman Filter (UKF), reducing the average RMSE by up to 74.98% and 60.76%, respectively, under severe Class D non-Gaussian excitations. Furthermore, the algorithm exhibits excellent disturbance rejection under transient speed bump impacts and maintains stable error reduction across vehicle speeds of 10–25 m/s. Ultimately, the APMCKF delivers high-precision estimation and exceptional robust stability under variable speeds and non-Gaussian disturbances.
Yunxing Liao, Zhaoxue Deng, Chong Peng et al.· World Electric Vehicle Journ...· 0 citations
In this paper, we propose a correntropy weighted extended Kalman filter (CWEKF) method to address the challenges of low estimation accuracy and poor robustness in sensorless rotor speed estimation for doubly-fed induction generators (DFIGs). Firstly, based on Faraday’s law of electromagnetic induction and the mechanical motion equation, we derive a DFIG nonlinear state-space model. This model quantifies the sources of nonlinearity arising from cross-coupling terms and product terms, providing a precise model foundation for rotor speed estimation. Secondly, we introduce correntropy theory to design a residual dynamic weighting scheme. By quantifying the local similarity between current and historical residuals, the scheme adaptively adjusts the noise covariance estimation weights, suppressing the interference of outdated data. Combined with the Chi-squared test, we derive an adaptive kernel bandwidth mechanism, balancing the response speed to noise variations and the estimation accuracy in steady-state. Additionally, we further integrate Huber robust weighting and regularization techniques for constructing a hybrid weighting mechanism and optimizing the covariance positive-definiteness correction to address the numerical stability deficiencies of the original algorithm. Using the Lipschitz condition and Lyapunov theory, we prove the mean-square exponential boundedness of the CWEKF estimation error. Finally, we build a DFIG vector control model using MATLAB R2021a and conduct comprehensive experiments, including simulation comparative experiments, open-loop speed identification experiments, and closed-loop sensorless control experiments. Comparative simulation experiments are conducted with EKF, AEKF, and RWEKF under three operating conditions; open-loop experiments verify that the constructed platform meets variable-speed constant-frequency (VSCF) power generation requirements, and closed-loop experiments compare CWEKF with MRAS under different speeds and parameter variations. The results show that the CWEKF has a maximum rotor speed estimation error <5 r/min, the response time has been reduced by over 65% compared to the traditional EKF, and it outperforms EKF, AEKF, RWEKF, and MRAS in estimation accuracy and stability, exhibiting significantly improved robustness under parameter variations and strong noise conditions.
The accurate estimation of State of Charge (SoC) is critical for safe, reliable, and optimistic operation of Battery Electric Vehicles (BEVs). Nevertheless, achieving robust SoC estimation is a big challenge because of nonlinear battery dynamics, parameter variability, sensor noise, and uncertain initial conditions. Standard techniques for measuring the state of charge (SoC), e.g., Coulomb Counting (CC) and Open Circuit Voltage (OCV), tend to drift and are not flexible in rapidly changing operating conditions. In this paper, we propose a model-based SoC estimation framework using an Extended Kalman Filter (EKF) and a second-order Thevenin equivalent circuit model. Previous works propose more sophisticated and hybrid estimation algorithms; the main contribution of this work is a systematic and reproducible assessment of classical EKF performance under various controlled scenarios with respect to disturbances caused by sensor noise, bias, and initialization errors. The EKF implementation is tested on the standardized driving cycles based on the Worldwide Harmonized Light-Duty Vehicle Test Procedure (WLTP), the Urban Dynamometer Driving Schedule (UDDS), and the Highway Fuel Economy Driving Schedule (HWFET) in a MATLAB-based simulation platform. A comparative study with Coulomb Counting is performed under identical disturbance conditions, which enables the identification of regions of operation where the EKF offers robustness benefits. Findings show that Coulomb Counting performs well under ideal conditions but degrades under realistic disturbances, whereas the EKF maintains stable and accurate state estimation, making it more reliable for BEV energy management.
Mazhar Hussain Shaik, Shafiq Ul Rehman, I. Ibrahim· Clean Energy Science and Tec...· 0 citations
A novel technique for load frequency control of a multi-area power system with state observer is proposed in this paper. State observer based model overcomes the difficulty in continuous system state measurements which forms the main drawback in the application of linear quadratic regulator. This paper presents a combined estimation of state and optimal gain so that uncertainties in the system and variation in system parameters can be successfully accommodated. Augmenting the optimal controller gains to state variables results in non-linearity in the system model and hence Extended Kalman filter is used for the combined estimation. A comparative analysis based on Monte Carlo simulations of a two area power system under different measurement noise variances are included for the validation of proposed control technique. Matlab Software for Disciplined Convex Programming toolbox is used to verify the convergence of proposed technique. To ensure the effectiveness of the proposed technique, it is applied to a control area linked with an interconnected system including real time simulations using PSCAD.
Anju G. Pillai, A. E. Daniel, E. Samuel· Engineering Research Express· 0 citations
This paper presents a unified comparative benchmark of four model-based sensorless speed-estimation algorithms for permanent magnet synchronous motor drives: Luenberger observer (LO), sliding-mode observer (SMO), model reference adaptive system (MRAS), and extended Kalman filter (EKF). The four estimators are implemented under the same PMSM model, operating profile, and simulation conditions. Their performance is evaluated using speed RMSE, position RMSE, steady-state error, maximum transient error, dq-axis current response, THD, disturbance rejection, and implementation complexity. The results show that the EKF achieves the highest estimation accuracy, whereas the SMO provides strong disturbance robustness. The LO and MRAS offer simpler implementation and lower computational cost, although MRAS exhibits larger transient errors. The comparative findings provide practical guidelines for selecting sensorless estimators according to the required trade-off among accuracy, robustness, harmonic behavior, and implementation cost.
Phan Nguyen Thanh Van, N. Tuan, H. Phi et al.· 2026 11th International Conf...· 0 citations
