Impact of PMU Measurement Buffer Length on State Estimation and its Optimization

Zhang, Qing; Chakhchoukh, Yacine; Vittal, Vijay; Heydt, G.T.; Logic, Naim; Sturgill, Steve

doi:10.1109/tpwrs.2012.2215890

Cited by 60 publications

(43 citation statements)

References 15 publications

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“…; exploitation of PMU samples available in between state reconstructions, to improve accuracy and reliability [20]; ; exploitation of non-synchronized but time-tagged SCADA measurements; ; a theoretical basis for selecting the weights of respectively synchrophasor, SCADA, and pseudo-measurements; ; diagnosis of unreliable state reconstructions caused by excessive transients; ; identification of persistently erroneous measurements; ; improved numerical solution of the optimization problem (for instance, infrequent updates of Hachtel's matrix); ; state reconstruction of larger systems; ; state reconstruction of a limited region but at higher rates, with the objective of tracking faster dynamics.…”

Section: Resultsmentioning

confidence: 99%

Tracking network state from combined SCADA and synchronized phasor measurements

Glavić

Cutsem

2013

2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid

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Initiated by the authors' previous work on state reconstruction from a limited number of synchrophasors [1], this paper goes one step further by exploring the possibility to track the network state using both SCADA and synchronized phasor measurements. When a SCADA measurement is received, it is used in the next state reconstruction; otherwise, it is replaced by a pseudo-measurement stemming from the previous state reconstruction. The approach resorts to a standard weighted least squares formulation and Hachtel's augmented matrix method. State reconstruction is intended to be used at a much higher rate than classical state estimation, for instance every second. It has been validated using simulated measurements obtained from detailed time simulation.

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Section: Resultsmentioning

confidence: 99%

Tracking network state from combined SCADA and synchronized phasor measurements

Glavić

Cutsem

2013

2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid

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“…Considering different sampling rates of the traditional and PMU measurements, a memory buffer of PMU measurements has been recommended to be installed in the state estimator. In [70], the problem that how buffering the phasor measurements can improve the state estimate has been investigated. Furthermore, the optimal buffer design and the use of the phasor measurements from that buffer have been addressed in [71].…”

Section: A Methodologiesmentioning

confidence: 99%

Recent advances on state estimation for power grids with unconventional measurements

Wang

Liu

et al. 2017

IET Control Theory & Applications

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State estimation problem for power systems has long been a fundamental issue that demands a variety of methodologies dependent on the system settings. With recent introduction of advanced devices of phasor measurement units (PMUs) and dedicated communication networks, the infrastructure of power grids has been greatly improved. Coupled with the infrastructure improvements are three emerging issues for the state estimation problems, namely, the coexistence of both traditional and PMU measurements, the incomplete information resulting from delayed, asynchronous and missing measurements due to communication constraints, and the cyber-attacks on the communication channels. In this paper, we aim to survey some recent advances on the state estimation methods which tackle the above three issues in power grids. Traditional state estimation methods applied in power grids are first introduced. Latest results on state estimation with mixed measurements and incomplete measurements are then discussed in great detail. In addition, the techniques developed to ensure the cyber-security of the state estimation schemes for power grids are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out.

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“…There are always different latencies for different sources of measurements which bring up the issue of time skewness. Many approaches have been proposed in the literature to tackle the issue [28]. In this work, assume that the issue is resolved using one of the solutions in the literature, and hence, the measurements are assumed to be synchronized.…”

Section: Available Measurements For Dssementioning

confidence: 99%

Data-Driven Learning-Based Optimization for Distribution System State Estimation

Zamzam

Sidiropoulos

2019

IEEE Trans. Power Syst.

148

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Distribution system state estimation (DSSE) is a core task for monitoring and control of distribution networks. Widely used algorithms such as Gauss-Newton perform poorly with the limited number of measurements typically available for DSSE, often require many iterations to obtain reasonable results, and sometimes fail to converge. DSSE is a non-convex problem, and working with a limited number of measurements further aggravates the situation, as indeterminacy induces multiple global (in addition to local) minima. Gauss-Newton is also known to be sensitive to initialization. Hence, the situation is far from ideal. It is therefore natural to ask if there is a smart way of initializing Gauss-Newton that will avoid these DSSE-specific pitfalls. This paper proposes using historical or simulation-derived data to train a shallow neural network to 'learn to initialize' -that is, map the available measurements to a point in the neighborhood of the true latent states (network voltages), which is used to initialize Gauss-Newton. It is shown that this hybrid machine learning / optimization approach yields superior performance in terms of stability, accuracy, and runtime efficiency, compared to conventional optimization-only approaches. It is also shown that judicious design of the neural network training cost function helps to improve the overall DSSE performance. Index TermsDistribution network state estimation, phasor measurement units, machine learning, neural networks, Gauss-Newton, least squares approximation.

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Impact of PMU Measurement Buffer Length on State Estimation and its Optimization

Cited by 60 publications

References 15 publications

Tracking network state from combined SCADA and synchronized phasor measurements

Tracking network state from combined SCADA and synchronized phasor measurements

Recent advances on state estimation for power grids with unconventional measurements

Data-Driven Learning-Based Optimization for Distribution System State Estimation

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