State forecasting in electric power systems

Silva, Armando M. Leite da; Filho, Milton Brown Do Coutto; Queiroz, J.F. de

doi:10.1049/ip-c.1983.0046

Cited by 185 publications

(86 citation statements)

References 8 publications

(15 reference statements)

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“…Authors in [5], [17], [18] and [19]- [23] have all used the Holt's LES technique [24] to obtain the values of F k and G k . In this case the equation (11) is reduces to a form:…”

Section: A Mathematical Modelingmentioning

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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“…Authors in [5], [17], [18] and [19]- [23] have all used the Holt's LES technique [24] to obtain the values of F k and G k . In this case the equation (11) is reduces to a form:…”

Section: A Mathematical Modelingmentioning

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 relationship between the states at instants k and k − 1 is described by using the matrix F and the vector g , which are updated using the Holt's method [17] …”

Section: Synchrophasor Assisted Hybrid State Estimatormentioning

confidence: 99%

A Synchrophasor Assisted Hybrid State Estimator

Kirinčić

Skok

Franković

2016

Journal of Electrical Engineering

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The paper presents a Synchrophasor Assisted Hybrid State Estimator that utilizes the conventional SCADA measurements and the synchrophasors obtained from Phasor Measurement Units (PMUs). To take advantage of the high sampling frequency of the multiple sets of synchrophasors they are preprocessed in a recursive algorithm that provides the state estimate for the power system part observable by PMUs. The results are forwarded to an iterative procedure in which they are combined with SCADA measurements. The given solution was applied on the IEEE test systems with 14, 30 and 57 buses and its performance was compared with other state estimators. The filtering of measurement errors and convergence, while providing improved accuracy of the final state estimates of the developed methodology can be compared with other hybrid state estimators.

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“…Different forecasting techniques can be applied to estimate F k , g k , and Q k . The Kalman filter in [16], exponential smoothing in [11], and artificial neural networks (ANN) in [17]- [18] have been utilized successfully under this context.…”

Section: Ekf-based Dsementioning

confidence: 99%

“…More details appear in [11]. Despite its rather simple implementation, this technique can offer very short-term predictions (few minutes ahead).…”

Section:  Step 1 -Parameter Identificationmentioning

confidence: 99%

“…Leite da Silva et al extended the approach with a focus on forecasting and filtering the state vectors using exponential smoothing and least-square estimation [11]. Since then, DSE has been known as an alternative state estimation approach that can predict state vectors one time step ahead based on a priori knowledge and that can be corrected with the subsequent measurement sets.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic state estimation for distribution networks with renewable energy integration

Nguyen¹,

Venayagamoorthy²,

Kling³

et al. 2013

SGCE

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The massive integration of variable and unpredictable Renewable Energy Sources (RES) and new types of load consumptions increases the dynamic and uncertain nature of the electricity grid. Emerging interests have focused on improving the monitoring capabilities of network operators so that they can have accurate insight into a network's status at the right moment and predict its future trends. Though state estimation is crucial for this purpose to trigger control functions, it has been used mainly for steady-state analysis. The need for dynamic state estimation (DSE), however, is increasing for real-time control and operation. This paper addresses the important role of DSE over conventional static-state estimation in this new distribution network context. Computational burden mitigates the state-of-the-art utilizations of DSE in real large-scale networks, although DSE was introduced several decades ago. This paper the unscented Kalman filter (UKF) to alleviate computational burden with DSE. The UKF-based approach does not use a linearization procedure and thus outperforms the conventional Extended Kalman Filter based approach to cope with non-linear models. The performance of the UKF method is investigated with a simulation of an 18-bus distribution network on the real-time digital simulator (RTDS) platform. A distribution network with considerable integration of renewable energy production is used to evaluate the UKF-based DSE approach under different types of events.

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State forecasting in electric power systems

Cited by 185 publications

References 8 publications

Power system tracking and dynamic state estimation

Power system tracking and dynamic state estimation

A Synchrophasor Assisted Hybrid State Estimator

Dynamic state estimation for distribution networks with renewable energy integration

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