A Dynamic Estimator for Complex Bus Voltage Determination

Srinivasan, K.; Robichaud, Y.

doi:10.1109/tpas.1974.293889

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…The authors in [5], [13]- [16], [19], [21]- [23], [26] and [27] have all used the Kalman filter or techniques based on Kalman filter for their DSE algorithms. Other techniques have also been proposed and implemented in the literature, though Kalman filter techniques seem to dominate in most of the DSE algorithms.…”

Section: State Filteringmentioning

confidence: 99%

Power system tracking and dynamic state estimation

Jain

Shivakumar

2009

2009 IEEE/PES Power Systems Conference and Exposition

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Abstract--State estimation is a key Energy Management System(EMS) function, responsible for estimating the state of the power system. Since state estimation is computationally expensive, it is not easy to execute it repetitively at short intervals, which is a requirement for real time monitoring and control. Hence in order to obtain a computationally inexpensive real time update of the state vector, tracking state estimation algorithms have been proposed and discussed in various research papers available in the literature. Tracking state estimation provides a fast real time update on the state of the power system with out any physical modeling of the time varying nature of the system. Dynamic state estimation models the time varying nature of the system, which allows it to predict the state vector in advance. Hence proves to be a major advantage in security analysis and other control functions. Various techniques for tracking and dynamic state estimation are available in the literature. This paper presents a bird's view on different methodologies and developments in both of these techniques based on our comprehensive survey of the available literature.Index Terms --Dynamic state estimation, kalman filter, phasor measurement unit, power systems, square root filter, state estimation, tracking state estimation.

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Section: State Filteringmentioning

confidence: 99%

Power system tracking and dynamic state estimation

Jain

Shivakumar

2009

2009 IEEE/PES Power Systems Conference and Exposition

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“…The dynamic model for FASE developed from oversimplified models. [5][6][7] The predictions provided by these methods are not accurate, but the basic structure of the FASE was established. Other simplified methods include the tracking estimators [8][9][10] in which the current states are assumed to be the same with their previous states.…”

Section: Introductionmentioning

confidence: 99%

An improved algorithm for cubature Kalman filter based forecasting‐aided state estimation and anomaly detection

Jin

Chakrabarti

et al. 2021

Int Trans Electr Energ Syst

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This article proposes a new algorithm for forecasting-aided state estimation based on the cubature Kalman filter (CKF) and new methods for detecting and identifying data anomalies. In this article, through extensive simulations, the CKF was compared to four different types of forecasting-aided state estimators (FASEs) including extended Kalman filter (EKF), iterated EKF, second-order Kalman filter and unscented Kalman filter under normal operation and bad data conditions. Identifying the challenge that the estimation accuracy of the existing CKF-based estimator is significantly lower than that of the other FASEs in the cases of sudden load change, and sudden topology change caused by faults an attempt to improve the CKF accuracy has been undertaken. The existing detection methods cannot accurately detect and distinguish those anomalies, and they cannot identify the anomaly location. This article proposes an improved algorithm for CKF-based FASE that overcomes the drawbacks of the existing CKF-based FASEs using a novel anomaly detection algorithm. The simulation results show that the new anomaly detection method is superior to the two existing anomaly detection algorithms. The simulations are performed in the above-mentioned four cases in IEEE 14 and 118 bus test systems in MATLAB.

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“…These algorithms are very efficient static estimators, from the computational point of view, without capability of predicting the state vector, as no dynamic model is explicitly assumed for the time behaviour of the system state. Simple dynamic models for the state vector behaviour, combined with linearised measurement equations, have been proposed [4,5] and the estimations have been achieved through Kalman filtering theory. Owing to the simplicity of these dynamic models, the ability of forecasting is basically at the same level of the tracking estimators.…”

mentioning

confidence: 99%

State forecasting in electric power systems

Silva¹,

Filho²,

Queiroz³

1983

IEE Proc. C Gener. Transm. Distrib. UK

185

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The state vector of a power system varies with time owing to the dynamic nature of system loads. Therefore, it is necessary to establish a dynamic model for the time evolution of the state vector. The dynamic state estimation approach consists of predicting the state vector based on past estimations, followed by a filtering process performed when a new set of measurements is available. This paper presents a new algorithm for forecasting and filtering the state vector, using exponential smoothing and least-squares estimation techniques. The proposed algorithm is compared with another one based on standard Kalman filtering theory. Numerical results showing the performance for both dynamic estimators under different operational conditions are presented and discussed. Detection and identification of multiple bad data are also included. The new dynamic estimator exploiting state forecasting is extremely useful to real-time monitoring of power systems.

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A Dynamic Estimator for Complex Bus Voltage Determination

Cited by 9 publications

References 9 publications

Power system tracking and dynamic state estimation

Power system tracking and dynamic state estimation

An improved algorithm for cubature Kalman filter based forecasting‐aided state estimation and anomaly detection

State forecasting in electric power systems

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