PMU-based Real-time Distribution System State Estimation Considering Anomaly Detection, Discrimination and Identification

Veerakumar, Nidarshan; Ćetenović, Dragan; Kongurai, Krit; Popov, Marjan; Jongepier, Arjen; Terzija, Vladimir

doi:10.1016/j.ijepes.2022.108916

Cited by 17 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But, when a dynamic process of variations in the power system is close to the stable value, the numerical stability of this method is poor. Another research work [28], explores the online state estimation for distribution grid for anomaly detection, discrimination and identification using EKF method with help real-time digital simulator. The errors contained in the network parameters are not considered in the study due to the complexity to deal with it.…”

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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…Analytical models rely on mathematical models to represent the underlying processes and relationships within a system [18,19]. These methods can be classified into three main categories: state estimation methods [20,21], parameter estimation methods [22,23], and equivalence space methods [24]. However, the powertrain systems of HEVs have become increasingly complex due to advancements in technology and the integration of various components such as internal combustion engines, electric motors, and sophisticated control systems.…”

Section: Introductionmentioning

confidence: 99%

Process monitoring in hybrid electric vehicles based on dynamic nonlinear method

Wang,

Deprizon,

Kit

et al. 2024

J Appl Eng Science

View full text Add to dashboard Cite

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

show abstract

“…It is necessary to comprehensively grasp the real-time information to guarantee the stable and safe operation of power system [3]. Power system state estimation (PSSE) can obtain the states of power system, which is fairly important for reliable operation of future power systems [4]. The traditional measurements for PSSE are usually collected from the supervisory control and data acquisition (SCADA) system, which has low sampling rates and second-level communication delays [5].…”

Section: Introductionmentioning

confidence: 99%

A distributed multi-area power system state estimation method based on generalized loss function

Chen

Liu

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution and the widely used estimator is the weighted least squares (WLS). However, the Gaussian distribution assumption is not always true and the performance of WLS becomes bad when the measurement noise is non-Gaussian. In this paper, a new distributed state estimation (SE) method is proposed for multi-area power systems. The proposed distributed method is based on the generalized loss function so that it can reduce the influence of non-Gaussian noise. Further, thanks to the matrix-splitting technology, the proposed distributed method can be implemented in a distributed way so that the computation time in each local area can be reduced. The simulation results carried out in the IEEE 30-bus and 118-bus systems verify the robustness and effectiveness of the proposed distributed SE method.

show abstract

PMU-based Real-time Distribution System State Estimation Considering Anomaly Detection, Discrimination and Identification

Cited by 17 publications

References 35 publications

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

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

Process monitoring in hybrid electric vehicles based on dynamic nonlinear method

A distributed multi-area power system state estimation method based on generalized loss function

Contact Info

Product

Resources

About