Power system dynamic state estimation and load modeling

Bila, Cem

doi:10.17760/d20003313

Cited by 5 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…transient dynamics, the power systems can be modeled as a set of nonlinear differential equations. Details on developing such models can be found in [2], [4], [19], [20]. In this and the following two sections, the problem and the proposed solution would be presented in a generic set-up, applicable for any (linear) dynamical systems.…”

Section: Detectormentioning

confidence: 99%

“…With the high frequency synchronized measurements from the Phasor Measurement Units (PMUs), the real-time and fast changing dynamic states of an interconnected power system can be estimated using Kalman filtering based techniques [1]- [4]. Starting from a known operating condition, the (noisy) dynamic states at each time-step are estimated by optimally combining the dynamical model-based predicted state-information with the real-time measurement data [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of Anomaly in the Dynamic State Estimator of a Power System using System Decomposition

Ghosal

2018

2018 European Control Conference (ECC)

View full text Add to dashboard Cite

In a state estimator, the presence of malicious or simply corrupt sensor data or bad data is detected by the high value of normalized measurement residuals that exceeds the threshold value, determined by the χ 2 distribution. However, high normalized residuals can also be caused by another type of anomaly, namely gross modeling or topology error. In this paper we propose a method to distinguish between these two sources of anomalies -1) malicious sensor data and 2) modeling error. The anomaly detector will start with assuming a case of malicious data and suspect some of the individual measurements corresponding to the highest normalized residuals to be 'malicious', unless proved otherwise. Then, choosing a change of basis, the state space is transformed and decomposed into 'observable' and 'unobservable' parts with respect to these 'suspicious' measurements. We argue that, while the anomaly due to malicious data can only affect the 'observable' part of the states, there exists no such restriction for anomalies due to modeling error. Numerical results illustrate how the proposed anomaly diagnosis based on Kalman decomposition can successfully distinguish between the two types of anomalies.

show abstract

Section: Detectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosis of Anomaly in the Dynamic State Estimator of a Power System using System Decomposition

Ghosal

2018

2018 European Control Conference (ECC)

View full text Add to dashboard Cite

show abstract

Power Systems Dynamic State Estimation With the Two-Step Fault Tolerant Extended Kalman Filtering

Wang

2021

IEEE Access

View full text Add to dashboard Cite

Bad data may lead to performance degradation or even instability of a power system, which can be caused by various factors: unintentional PMU abnormalities, topology error, malicious cyber-attacks, electromagnetic interference, temporary loss of communication links, external disturbances, extraneous noise biases, etc. In order to develop a more resilient and reliable state estimation technique, this manuscript presents a novel two-step fault tolerant extended Kalman filter framework for discrete-time stochastic power systems, under bad data, PMU failures, external disturbances, extraneous noise, and bounded observer-gain perturbation conditions. The failure mechanisms of multiple phasor measurement units are assumed to be independent of each other with various bad data or malfunction rates. The benchmark IEEE standard test systems are utilized as a demonstrative example to carry out computer simulation studies and to examine different estimation algorithms. Experimental results demonstrates that the proposed second-order fault tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first-and second-order extended Kalman filter, and the unscented Kalman filter. The proposed two-step fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing dynamic power system state estimation techniques. INDEX TERMSDynamic state estimation, phasor measurement unit, extended Kalman filtering, bad data, sensor failures

show abstract

Fusion of PMU and SCADA Data for dynamic state estimation of power system

Ghosal

Rao

2015

2015 North American Power Symposium (NAPS)

View full text Add to dashboard Cite

Power system dynamic state estimation and load modeling

Cited by 5 publications

References 44 publications

Diagnosis of Anomaly in the Dynamic State Estimator of a Power System using System Decomposition

Diagnosis of Anomaly in the Dynamic State Estimator of a Power System using System Decomposition

Power Systems Dynamic State Estimation With the Two-Step Fault Tolerant Extended Kalman Filtering

Fusion of PMU and SCADA Data for dynamic state estimation of power system

Contact Info

Product

Resources

About