Optimal Placement of Phasor Measurement Units via Convex Relaxation

Sensor selection for estimation, filtering, and detection

“…The above problem can also be transformed to an SDP, as in (6). When the state-space equations follow a linear model y t := H t θ t + n t , i.e., for a discrete-time linear dynamical system, the PCRB is independent of the state.…”

“…Sensor deployment can also be interpreted as a sensor selection problem in which the best subset of the available sensor locations are selected subject to a performance constraint. Sensor selection has been applied to a wide variety of problems: dynamical systems [1]- [5], network monitoring [6], field estimation [7], array optimization [8], sourceinformative sensor identification [9], anchor placement [10], and outlier detection [11].…”

Indicadores estáticos normalizados para estabilidad de tensión basados en reservas de potencia reactiva y en márgenes de cargabilidad

“…De otro lado, se introducirá cómo podrían integrarse los indicadores propuestos en visualizaciones desarrolladas bajo conceptos de Conciencia Situacional (SA del inglés Situational Awareness). Es de aclarar que el concepto de SA está siendo ampliamente concebido en la actualidad para su uso generalizado en aplicaciones de potencia eléctrica, dada la masificación de unidades de medición fasorial o sincrofasores (PMU del inglés Phasor Measurement Units) [10][11][12][13][14][15].…”

Sparsity-promoting adaptive sensor selection for non-linear filtering

“…There exists a vast literature on sensor selection [1, and reference therein], and it has been applied to a wide variety of problems: dynamical systems [2][3][4][5][6], power grid monitoring [7], field estimation [8], and reliable sensor selection [9]. The sensor selection probThis work was supported in part by STW under FASTCOM project (10551) and in part by NWO-STW under the VICI program (10382).…”

“…The sensor selection probThis work was supported in part by STW under FASTCOM project (10551) and in part by NWO-STW under the VICI program (10382). lem is typically formulated as an optimal experimental design problem [10] in which a scalar cost related to the mean squared error (MSE) covariance matrix is optimized to select the best subset of K sensors, where K is typically known [1,7]. This is a non-convex Boolean optimization problem which incurs a combinatorial search over all the K M possible combinations, and hence, it is often simplified via convex relaxation techniques [1] or solved using greedy heuristics.…”