Dynamic state estimation including anomaly detection and identification for power systems

Nishiya, Ken-ichi; Hasegawa, Jun; Koike, Toichiro

doi:10.1049/ip-c.1982.0032

Cited by 107 publications

(42 citation statements)

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“…As shown in Fig. 6, for the first two seconds, the BD does not detect the anomaly since the obtained residuals are within the bounds defined by (12). As soon as this condition is not satisfied, i.e.…”

Section: ) Small Deviationsmentioning

confidence: 96%

A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs

Pignati

Zanni

Sarri

et al. 2014

2014 Power Systems Computation Conference

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Abstract-The paper proposes a specific algorithm for the pre-estimation filtering of bad data (BD) in PMU-based power systems linear State Estimators (SEs). The approach is framed in the context of the so-called real-time SEs that take advantage of the high measurement frame rate made available by PMUs (i.e., 50 -60 frames per second). In particular, the proposed algorithm examines PMU measurement innovations for each new received set of data in order to locate anomalies and apply countermeasures. The detection and identification scheme is based on: (i) the forecasted state of the network obtained by means of a linear Kalman filter, (ii) the current network topology, (iii) the accuracy of the measurement devices and (iv) their location. The incoming measurement from each PMU is considered reliable, or not, according to a dynamic threshold defined as a function of the confidence of the predicted state estimated by using an AutoRegressive Integrated Moving Average (ARIMA) process. The performances of the proposed algorithm are validated with respect to single and multiple bad data of different nature and magnitudes. Furthermore, the algorithm is also tested against faults occurring in the power system to show its robustness during these unexpected operating conditions.

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Section: ) Small Deviationsmentioning

confidence: 96%

A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs

Pignati

Zanni

Sarri

et al. 2014

2014 Power Systems Computation Conference

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“…Parameter Identification F k , G k and Q are the parameters to be calculated online to evaluate the dynamic model shown in (11). Debs and Larson [13], credited with the seminal paper on DSE, and Nishiya et al [14] have assume a simple linear model with F k assumed to be an identity matrix and G k assumed to be zero. But this makes the estimator very simplistic and hampers the forecasting ability of the estimator [1].…”

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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“…The time evolution of the state vector deviation is given by: £2 , fc+i _ F k cc k + tv k (10) where F k is a nonzero matrix with dimension (n x n); w k is a white Gaussian sequence with zero mean and covariance matrix Q k . Note that eqn.…”

Section: Dynamic Model II [6 8] (Dm-ii)mentioning

confidence: 99%

State forecasting in electric power systems

Silva¹,

Filho²,

Queiroz³

1983

IEE Proc. C Gener. Transm. Distrib. UK

178

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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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Dynamic state estimation including anomaly detection and identification for power systems

Cited by 107 publications

References 0 publications

A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs

A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs

Power system tracking and dynamic state estimation

State forecasting in electric power systems

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