Dynamic‐State Estimation

Abur, Ali

doi:10.1002/9781118755471.sgd070

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Play-back Function via PSSE/PSLF/PowerWorld/DSATools™ Filtering step 1 1 , , , It is clear from Fig. 2 that the implementation of DSE has two main steps, namely the state prediction via (1) to obtain predicted state at time k and the state correction step that integrates the state prediction and measurements in (2) for filtering.…”

Section: Practical Implementation Of Dsementioning

confidence: 99%

“…SSE has become a widely used tool in today's EMS, while DSE is a new tool for the industry and system operators. It 1 Topological observability analysis provides a binary answer as the first byproduct, but it gives additional valuable information, such as which buses (islands) are observable or which pseudo-measurements should minimally be added to restore full observability. Numerical observability analysis, being based on the factorization of the Jacobian or Gain matrices, provides a "spectrum" or range of observability answers, depending on the condition number of the matrix being factorized.…”

Section: Comparative Overview Of Sse and Dsementioning

confidence: 99%

“…D YNAMIC state estimation (DSE) [1] is going to be very useful for time critical monitoring, control, and protection of future electric power grids. This is largely due to the changes in generation mixes and load compositions, particularly the increasing penetration of intermittent, stochastic and power electronics-interfaced non-synchronous renewable generation and distributed energy resources (DERs) [2].…”

Section: Introductionmentioning

confidence: 99%

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Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Zhao

Netto

Huang

et al. 2021

IEEE Trans. Power Syst.

Self Cite

163

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Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, timesynchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving This work was partially supported by US Department of Energy under Advanced Grid Modernization Program.

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Section: Practical Implementation Of Dsementioning

confidence: 99%

Section: Comparative Overview Of Sse and Dsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Zhao

Netto

Huang

et al. 2021

IEEE Trans. Power Syst.

Self Cite

163

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show abstract

“…Achieving (strong) observability [129,209] for dynamic state estimation represents a major effort, a line of research that has been pursued [130,[132][133][134]. In all previous works, the synergy between static and dynamic state estimation, briefly discussed in [210], has been completely neglected. Also, none of the previous works considered placing merging units in addition to phasor measurement units.…”

Section: Future Directionsmentioning

confidence: 99%

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Netto

Krishnan²,

Zhang

et al. 2021

IET Generation Trans & Dist

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Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimisation model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large-scale, long-term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics; therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernisation of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research-and-development needs to push the boundaries for innovation. Accordingly, this paper first reviews the state-of-the-art considering both transmission and distribution grids. Then, it consolidates the key factors to be considered in the problem formulation. Finally, it provides a set of perspectives on the measurement placement problem, and it concludes with future research directions.

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“…To achieve (strong) observability [130,210] for dynamic state estimation represents a major effort, a line of research that has been pursued [131,[133][134][135]. In all previous works, the synergy between static and dynamic state estimation, briefly discussed in [211], has been completely neglected. Also, none of the previous works considered placing merging units in addition to phasor measurement units.…”

Section: Future Directionsmentioning

confidence: 99%

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Netto,

Krishnan,

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimization model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large-scale, long-term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics. Therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernization of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research-and-development needs to push the boundaries for innovation. Accordingly, this paper first reviews the state of the art considering both transmission and distribution grids. Then, it consolidates the key factors to be considered in the problem formulation. Finally, it provides a set of perspectives on the measurement placement problem, and it concludes with future research directions.• H Conjugate transpose of the vector or matrix • | • | Magnitude of a complex-valued variable • card( • ) Cardinality, i.e., the number of elements, of a set • det( • ) Determinant of a matrix • Im( • ) Imaginary part of the complex-valued variable • Re( • ) Real part of the complex-valued variable • * https:// gmlc.doe.gov/ * * https:// globalpst.org/ Measurement placement

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Dynamic‐State Estimation

Cited by 5 publications

References 17 publications

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

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