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1.发明申请公开(公告)号：US20210357703A1
公开(公告)日：2021-11-18
申请号：US17318046
申请日：2021-05-12
申请人： Jie Fan , Sunil Rao , Gowtham Muniraju , Cihan Tepedelenlioglu , Andreas Spanias
发明人： Jie Fan , Sunil Rao , Gowtham Muniraju , Cihan Tepedelenlioglu , Andreas Spanias
摘要： Various embodiments of a system and associated method for detecting and classifying faults in a photovoltaic array using graph-based signal processing.
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公开(公告)号：US11765609B2
公开(公告)日：2023-09-19
申请号：US17345652
申请日：2021-06-11
摘要： A system estimates spectral radius and leverages local updates from neighboring nodes in a wireless network to iteratively update state values of each node in the network and estimate a spectral radius of the network with guaranteed convergence. A method associated with the system method is a distributed method that efficiently converges to an invertible function of the spectral radius based only on local communications of the network for digital communication models in the presence and/or absence of packet loss, as opposed to conventional centralized methods.
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公开(公告)号：US11490286B2
公开(公告)日：2022-11-01
申请号：US17146130
申请日：2021-01-11
摘要： Various embodiments of systems and methods for robust max consensus for wireless sensor networks in the presence of additive noise by determining and removing a growth rate estimate from state values of each node in a wireless sensor network are disclosed.
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公开(公告)号：US20210392529A1
公开(公告)日：2021-12-16
申请号：US17345652
申请日：2021-06-11
IPC分类号： H04W24/08
摘要： Various embodiments of a system and associated method for estimating a consensus driven distributed a spectral radius of a wireless sensor network are disclosed herein.
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公开(公告)号：US20210390413A1
公开(公告)日：2021-12-16
申请号：US17348410
申请日：2021-06-15
摘要： Dropout and pruned neural networks for fault classification in photovoltaic (PV) arrays are provided. Automatic detection of solar array faults leads to reduced maintenance costs and increased efficiencies. Embodiments described herein address the problem of fault detection, localization, and classification in utility-scale PV arrays. More specifically, neural networks are developed for fault classification, which have been trained using dropout regularizers. These neural networks are examined and assessed, then compared with other classification algorithms. In order to classify a wide variety of faults, a set of unique features are extracted from PV array measurements and used as inputs to a neural network. Example approaches to neural network pruning are described, illustrating trade-offs between model accuracy and complexity. This approach promises to improve the accuracy of fault classification and elevate the efficiency of PV arrays.
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公开(公告)号：US20210219167A1
公开(公告)日：2021-07-15
申请号：US17146130
申请日：2021-01-11
摘要： Various embodiments of systems and methods for robust max consensus for wireless sensor networks in the presence of additive noise by determining and removing a growth rate estimate from state values of each node in a wireless sensor network are disclosed.
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