Assessing strengths and weaknesses of measurement sets for state estimation

Augusto, André Guimarães; Guimaraens, Marcio Andre Ribeiro; Filho, Milton Brown Do Coutto; Souza, Julio Cesar Stacchini de

doi:10.1109/ptc.2017.7980839

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Computing Cks has gained ground, especially considering its utilization favors reliable and uninterrupted monitoring. For instance, extCA can be applied for the following practical purposes: as a handy commissioning/diagnostic tool involving BD processing during SE deployment 19 ; to evaluate how exposed SE is considering the currently available measurements 20,21 ; to analyze the occurrence of Cks composed of network parameters and the resultant impossibility of identifying errors in such parameters 22 ; to influence the expansion of measuring systems in an optimized way, reducing the criticality level of the measurements 23 ; to assist in constructing defense strategies, by pointing out weak spots (critical data) in the measuring system, to favor preventive actions-for example, protecting measurements that integrate Cks and Ck-sets-in case a cyber-attack occurs. 24 With these applications, extCA aligns itself closely with the following somewhat philosophical principle: tackling a problem in time is more valuable than seeking a remedy after the damage.…”

Section: Scope Motivation and Contributionsmentioning

confidence: 99%

GPU parallel processing to enable extensive criticality analysis in state estimation

Nishio,

Do Coutto Filho,

Stachinni de Souza

et al. 2024

Concurrency and Computation

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SummaryPower system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far.

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Section: Scope Motivation and Contributionsmentioning

confidence: 99%

GPU parallel processing to enable extensive criticality analysis in state estimation

Nishio,

Do Coutto Filho,

Stachinni de Souza

et al. 2024

Concurrency and Computation

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GPU-Based Criticality Analysis Applied to Power System State Estimation

Silva

Clua

Filho

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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State Estimation (SE) is one of the main tools in the operation of power systems. Its primary role is to filter statistically small errors and to eliminate spurious measurements. SE requires an adequate number of measurements, varied, and strategically distributed in the power grid. Criticality analysis consists of identifying which combinations of measurements, forming tuples of different cardinalities, are essential for observing the power network as a whole. If a tuple of measurements becomes unavailable and, consequently, unobservable, the tuple is considered critical. The observability condition is verified by the factorization of the residual covariance matrix, which usually is a time-consuming computation task. Also, the search for criticalities is costly, being a combinatorial based problem. This paper proposes a parallel approach of the criticality analysis in the SE realm, through multi-threads execution on CPU and GPU environments. To date, no publication reporting the use of GPU for computing critical elements of the SE process is found in the specialized literature. Numerical results from simulations performed on the IEEE 14- and 30-bus test systems showed speed-ups close to 25 , when compared with parallel CPU architectures .

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