Dynamic anomaly localization of utility pole wires

    公开(公告)号:US12160090B2

    公开(公告)日:2024-12-03

    申请号:US18492902

    申请日:2023-10-24

    Abstract: Systems and methods for performing the dynamic anomaly localization of utility pole aerial/suspended/supported wires/cables by distributed fiber optic sensing. In sharp contrast to the prior art, our inventive systems and methods according to aspects of the present disclosure advantageously identify a “location region” on a utility pole supporting an affected wire/cable, thereby permitting the identification and reporting of service personnel that are uniquely responsible for responding to such anomalous condition(s).

    UNSUPERVISED TRANSFORMER SIGNAL SEPARATION

    公开(公告)号:US20250146861A1

    公开(公告)日:2025-05-08

    申请号:US18917504

    申请日:2024-10-16

    Abstract: Systems and methods include collecting vibration data along an optical fiber cable using distributed acoustic sensing (DAS). The collected vibration data is preprocessed to separate the vibration data into at least two mixtures. The at least two mixtures are combined into a mixture of mixtures. The mixture of mixtures is separated into a plurality of estimated source signals using a separation model. The separation model is trained using an unsupervised loss computed between the estimated source signals and the at least two mixtures.

    Contrastive learning of utility pole representations from distributed acoustic sensing signals

    公开(公告)号:US11698290B2

    公开(公告)日:2023-07-11

    申请号:US17714091

    申请日:2022-04-05

    CPC classification number: G01H9/004

    Abstract: Systems and methods for operating a distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system include a length of optical sensing fiber suspended aerially by a plurality of utility poles and in optical communication with a DFOS interrogator/analyzer. The method includes operating the DFOS/DAS system while manually exciting more than one of the poles to obtain frequency response(s) of the excited poles; contrastively training a convolutional neural network (CNN) with the frequency responses obtained; classifying the utility poles using the contrastively trained CNN; and generating a profile map of the excited poles indicative of the classified utility poles.

    RISK MITIGATION SYSTEM FOR ELECTRICAL POWER GRIDS

    公开(公告)号:US20250149887A1

    公开(公告)日:2025-05-08

    申请号:US18937680

    申请日:2024-11-05

    Abstract: Systems and methods for a risk mitigation system for electrical power grids. To mitigate risks such as natural destructive forces, collected risk data and EPG data can be fused to obtain fused data. The vulnerability metric and fragility metric of the EPG based on risk profiles generated from the fused data can be predicted with a physics-informed neural network (PINN) trained with the fused data. EPG threat metrics can be developed by integrating the vulnerability metric, fragility metric, and the risk profiles into an integrated score that determines the probability of failure of the EPG caused by natural destructive forces. The present embodiments can perform a corrective action with an automated helper to mitigate the risks to the EPG caused by the natural destructive forces determined from the EPG threat metrics.

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