Whither dynamic state estimation?

Rousseaux, Patricia; Cutsem, Th. Van; Liacco, T.E. Dy

doi:10.1016/0142-0615(90)90006-w

Cited by 55 publications

(22 citation statements)

References 10 publications

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“…Finally, the tangent vector can be calculated for each predicted state using (3). For one step-ahead, one has: (11) This procedure allows voltage security monitoring through the computation of the tangent vector for fixture load scenarios, obtained considering load trends all over the system. Once the future operating scenarios are established, a load flow program can be executed to check the existence of power flow solutions.…”

Section: Proposed Methodologymentioning

confidence: 99%

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On-line voltage stability monitoring

Souza¹,

Souza

Silva³

2000

IEEE Trans. Power Syst.

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This paper presents a new methodology for online voltage stability assessment, consisting of two steps. Firstly, the time evolution of the power system operating state is modeled with the help of a forecasting-aided state estimator; secondly the voltage collapse point is determined through an extrapolation technique based on tangent vector behavior. Tests are carried out using the IEEE-14 bus system, where different operating scenarios are considered. The results show that monitoring system load trends may improve the assessment of voltage stability.

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Section: Proposed Methodologymentioning

confidence: 99%

“…The other parameter to be estimated is the covariance matrix . This can be achieved by an off-line simulation procedure [10], [11].…”

Section: A Modeling Of System Dynamicsmentioning

confidence: 99%

On-line voltage stability monitoring

Souza¹,

Souza

Silva³

2000

IEEE Trans. Power Syst.

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“…Most of the algorithms of dynamic state estimation are based on EKF [Rousseaux et al, 1990], which provides a linear minimum variance estimate of the state vector (x ). Some of the features of the EKF method are as follows:…”

Section: State Forecasting In Electric Power Systemmentioning

confidence: 99%

“…In the literature the dynamic behavior of the power system states (x ) has been modeled in three diOE erent ways [Rousseaux et al, 1990;Rousseaux et al, 1988]. The rst model directly uses the dynamic modeling of the conventional state variables.…”

Section: Introductionmentioning

confidence: 99%

“…The state vector, which consists of voltage magnitudes and phase angles at all the nodes, varies with time, owing to the dynamic nature of the system loads. Therefore it is necessary to establish a dynamic model for the time behavior of the power system states, whereas the dynamic modeling and detailed representations of power plants are outside the scope of the state forecasting problem [Rousseaux et al, 1990]. …”

Section: Introductionmentioning

confidence: 99%

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Power System State Forecasting Using Artificial Neural Networks

Kumar¹,

Srivastava²

1999

Electric Machines & Power Systems

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This paper presents a new method for power system state forecasting using arti cial neural networks (ANN). The state forecasting problem has been solved in two steps: the ltering step and the forecasting step in an open loop con guration. Because under normal operating conditions the power system behaves in a quasi-static manner, a simpli ed model of the dynamic behavior of the power system states is considered. Two diOE erent ANN models have been used for these two steps of power system state forecasting problem. For the ltering step, a functional link network (FLN), and for the forecasting step, a time delay neural network (TDNN) have been used to simulate the dynamic behavior of the power system states. The proposed method has been tested on two IEEE test systems, and a practical Indian system and results have been compared with an extended Kalman lter (EKF) based technique [Leite da Silva et al., 1983].

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Estimation Based on Multiple Scans of Measurements

Monticelli

1999

State Estimation in Electric Power Systems

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Whither dynamic state estimation?

Cited by 55 publications

References 10 publications

On-line voltage stability monitoring

On-line voltage stability monitoring

Power System State Forecasting Using Artificial Neural Networks

Estimation Based on Multiple Scans of Measurements

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