Data‐driven sensor placement for state reconstruction via POD analysis

Castillo, Alejandro; Messina, A.R.

doi:10.1049/iet-gtd.2019.0199

Cited by 12 publications

(8 citation statements)

References 34 publications

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“…Determining the optimal sensors' locations for signal reconstruction is still an open challenge that numerous studies have attempted to solve by different methods (e.g., Manohar et al (2018); Castillo and Messina (2019); Yildirim et al (2009); Annoni et al (2018); Caselton and Zidek (1984); Krause et al (2008); Joshi and Boyd (2008); Powell et al). Three of these methods will serve as baseline of the present study.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…Zhang and Bellingham (2008) added to the EOFs extrema method a constraint on the cross products of EOFs to select the sensors' locations, and applied it to Pacific sea surface temperature reconstruction. Using the same kind of constrained EOFs analysis, Castillo and Messina (2019) proposed a data-driven framework based on a Proper Orthogonal Decomposition to determine the optimal locations for power system oscillation monitoring and state reconstruction. In this study they selected iteratively the locations with highest POD amplitude and lowest cross coupling between the modes.…”

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confidence: 99%

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Gaussian Mixture Models for the Optimal Sparse Sampling of Offshore Wind Resource

Marcille

Thiébaut

Filipot

2022

Preprint

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Abstract. Offshore wind resource assessment is a crucial step for the development of offshore wind energy. It relies on the installation of measurement devices, which placement is an open challenge for developers. In this paper, a sparse sampling method using a Gaussian Mixture Model on Numerical Weather Prediction data is developed for the offshore wind reconstruction. It is applied on France's main offshore wind energy development areas, Normandy, Southern Brittany, and the Mediterranean Sea. The study is based on 3 years of Meteo France AROME's data, available through the MeteoNet data-set. Using a Gaussian Mixture Model for data clustering, it yields to optimal sensors' locations with regards to wind field reconstruction error. The proposed workflow is described and compared to state-of-the-art methods for sparse sampling. It constitutes a robust yet simple method for the definition of optimal sensor siting for offshore wind reconstruction. The described method yields to optimal network of 7, 4, and 4 sensors for Normandy, Southern Brittany and the Mediterranean Sea with a gain of approximately 20 % in wind field reconstruction error compared to the median Monte Carlo case, and more than 30 % compared to state-of-the-art methods.

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Section: Baseline Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Gaussian Mixture Models for the Optimal Sparse Sampling of Offshore Wind Resource

Marcille

Thiébaut

Filipot

2022

Preprint

View full text Add to dashboard Cite

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“…The use of the Apache Spark or Kafka platforms is also gaining traction among researchers and the power industry to develop a scalable, heterogeneous data ingestion and distributed analytics platform. Entergy 2285 [83,84] Poland #1 2383 [40,43,54,61,62,103,107,119] Poland #2 2746 [38,43] Poland #3 3120 [110] Poland #4 3375 [64] USA #3 4520 [85] Mexico #2 5449 [144] USA #4 8000 [92] add further richness to the data by using offline modelgenerated data, especially to capture the influence of lowprobability, high-impact events and to detect unforeseen patterns. This is particularly useful for applications that are geared towards detecting anomalous data, cybersecurity intrusions, and ensuring grid resilience-something for which not many past works have designed optimal measurement placement methods, as shown from the reviews in Sections 2 and 3.…”

Section: Future Directionsmentioning

confidence: 99%

“…The idea is that buses with a strong correspondence to the largest Lyapunov exponent are more significant from the system stability standpoint, and they should be made observable with higher priority. See also [144]. The drawback of these approaches lies in their dependence on an explicit energy function, which is challenging to obtain.…”

Section: Measurement Placement In Transmission Gridsmentioning

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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“…The idea is that buses with a strong correspondence to the largest Lyapunov exponent are more significant from the system stability standpoint, and they should be made observable with higher priority. See also [145]. The drawback of these approaches lies in their dependency on an explicit energy function, which is challenging to obtain.…”

Section: Voltage Control In Transmission Gridsmentioning

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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Data‐driven sensor placement for state reconstruction via POD analysis

Cited by 12 publications

References 34 publications

Gaussian Mixture Models for the Optimal Sparse Sampling of Offshore Wind Resource

Gaussian Mixture Models for the Optimal Sparse Sampling of Offshore Wind Resource

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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