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1. 发明授权

US11621668B2 Solar array fault detection, classification, and localization using deep neural nets 有权
公开(公告)号：US11621668B2
公开(公告)日：2023-04-04
申请号：US16868050
申请日：2020-05-06
申请人： Sunil Srinivasa Manjanbail Rao , Andreas Spanias , Cihan Tepedelenlioglu
发明人： Sunil Srinivasa Manjanbail Rao , Andreas Spanias , Cihan Tepedelenlioglu
IPC分类号： G01R31/28 , H02S50/10 , G06N3/084
摘要： Solar array fault detection, classification, and localization using deep neural nets is provided. A fault-identifying neural network uses a cyber-physical system (CPS) approach to fault detection in photovoltaic (PV) arrays. Customized neural network algorithms are deployed in feedforward neural networks for fault detection and identification from monitoring devices that sense data and actuate each individual module in a PV array. This approach improves efficiency by detecting and classifying a wide variety of faults and commonly occurring conditions (e.g., eight faults/conditions concurrently) that affect power output in utility scale PV arrays.

2. 发明申请

US20210390413A1 DROPOUT AND PRUNED NEURAL NETWORKS FOR FAULT CLASSIFICATION IN PHOTOVOLTAIC ARRAYS 有权
公开(公告)号：US20210390413A1
公开(公告)日：2021-12-16
申请号：US17348410
申请日：2021-06-15
申请人： Andreas Spanias , Sunil Srinivasa Manjanbail Rao , Gowtham Muniraju , Cihan Tepedelenlioglu
发明人： Andreas Spanias , Sunil Srinivasa Manjanbail Rao , Gowtham Muniraju , Cihan Tepedelenlioglu
IPC分类号： G06N3/08 , G06K9/62 , H02S50/10
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.

3. 发明授权

US09507011B2 Maximum likelihood localization in the presence of channel uncertainties 有权
标题翻译：存在通道不确定性时的最大似然定位
公开(公告)号：US09507011B2
公开(公告)日：2016-11-29
申请号：US14213655
申请日：2014-03-14
申请人： Xue Zhang , Cihan Tepedelenlioglu , Mahesh K. Banavar , Andreas Spanias
发明人： Xue Zhang , Cihan Tepedelenlioglu , Mahesh K. Banavar , Andreas Spanias
IPC分类号： G01S5/02 , G01S5/14 , H04W84/18
CPC分类号： G01S5/0278 , G01S5/0289 , G01S5/14 , H04W84/18
摘要： Some embodiments include a wireless sensor network system. Other embodiments of related systems and methods are also disclosed.
摘要翻译：一些实施例包括无线传感器网络系统。还公开了相关系统和方法的其它实施例。

4. 发明申请

US20210219167A1 SYSTEMS AND METHODS FOR ROBUST MAX CONSENSUS FOR WIRELESS SENSOR NETWORKS 有权
公开(公告)号：US20210219167A1
公开(公告)日：2021-07-15
申请号：US17146130
申请日：2021-01-11
申请人： Gowtham Muniraju , Cihan Tepedelenlioglu , Andreas Spanias
发明人： Gowtham Muniraju , Cihan Tepedelenlioglu , Andreas Spanias
IPC分类号： H04W28/02 , H04W84/18 , H04W74/00 , H04W4/38
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.

5. 发明授权

US10440553B2 Distributed network center and area estimation 有权
公开(公告)号：US10440553B2
公开(公告)日：2019-10-08
申请号：US15995433
申请日：2018-06-01
申请人： Sai Zhang , Cihan Tepedelenlioglu , Andreas Spanias
发明人： Sai Zhang , Cihan Tepedelenlioglu , Andreas Spanias
IPC分类号： H04W8/14 , H04W84/18 , H04W4/38 , H04W8/00 , H04L29/08 , H04W24/02
摘要： Some embodiments include a wireless sensor network including a plurality of sensor nodes each comprising: a signal receiver configured to receive intermediate information from at least one of one or more neighboring nodes of the plurality of sensor nodes, one or more processors configured to receive the intermediate information and update the intermediate information based on a soft-max approximation function, and a transmitter configured to send the intermediate information, as updated, to at least one of the one or more neighboring nodes of the plurality of sensor nodes. For each sensor node of the plurality of sensor nodes: the sensor node can store local location coordinates for the sensor node, and the sensor node can be devoid of receiving location coordinates for any other of the plurality of sensor nodes. The plurality of sensor nodes can be configured to communicate in a distributed manner for a first plurality of iterations until a final iteration of the first plurality of iterations when a predetermined stopping condition is satisfied. The plurality of sensor nodes can be further configured to generate an estimated center of the wireless sensor network based on the intermediate information updated in the final iteration of the first plurality of iterations. The wireless sensor network can be devoid of a fusion center. Other embodiments are disclosed.

6. 发明授权

US11490286B2 Systems and methods for robust max consensus for wireless sensor networks 有权
公开(公告)号：US11490286B2
公开(公告)日：2022-11-01
申请号：US17146130
申请日：2021-01-11
申请人： Gowtham Muniraju , Cihan Tepedelenlioglu , Andreas Spanias
发明人： Gowtham Muniraju , Cihan Tepedelenlioglu , Andreas Spanias
IPC分类号： H04W28/02 , H04W84/18 , H04W4/38 , H04W74/00
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.

7. 发明申请

US20210392529A1 SYSTEMS AND METHODS FOR CONSENSUS-BASED DISTRIBUTED GRAPH SPECTRAL RADIUS ESTIMATION 有权
公开(公告)号：US20210392529A1
公开(公告)日：2021-12-16
申请号：US17345652
申请日：2021-06-11
申请人： Andreas Spanias , Cihan Tepedelenlioglu , Gowtham Muniraju
发明人： Andreas Spanias , Cihan Tepedelenlioglu , Gowtham Muniraju
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.

8. 发明申请

US20180197046A1 METHODS, APPARATUSES, AND SYSTEMS FOR RECONSTRUCTION-FREE IMAGE RECOGNITION FROM COMPRESSIVE SENSORS 审中-公开
公开(公告)号：US20180197046A1
公开(公告)日：2018-07-12
申请号：US15869780
申请日：2018-01-12
申请人： Henry Braun , Pavan Turaga , Andreas Spanias , Cihan Tepedelenlioglu
发明人： Henry Braun , Pavan Turaga , Andreas Spanias , Cihan Tepedelenlioglu
IPC分类号： G06K9/62 , G06N3/08
CPC分类号： G06K9/6255 , G06K9/20 , G06K9/4628 , G06K9/6231 , G06K9/6268 , G06K9/627 , G06N3/0445 , G06N3/0454 , G06N3/0472 , G06N3/08 , G06N3/084
摘要： The disclosure relates to an image recognition algorithm implemented by a hardware control system which operates directly on data from a compressed sensing camera. A computationally expensive image reconstruction step can be avoided, allowing faster operation and reducing the computing requirements of the system. The method may implement an algorithm that can operate at speeds comparable to an equivalent approach operating on a conventional camera's output. In addition, at high compression ratios, the algorithm can outperform approaches in which an image is first reconstructed and then classified.

9. 发明申请

US20200274484A1 SYSTEMS AND METHODS FOR CONNECTION TOPOLOGY OPTIMIZATION IN PHOTOVOLTAIC ARRAYS USING NEURAL NETWORKS 审中-公开
公开(公告)号：US20200274484A1
公开(公告)日：2020-08-27
申请号：US16797281
申请日：2020-02-21
申请人： Vivek Sivaraman Narayanaswamy , Andreas Spanias , Rajapandian Ayyanar , Cihan Tepedelenlioglu
发明人： Vivek Sivaraman Narayanaswamy , Andreas Spanias , Rajapandian Ayyanar , Cihan Tepedelenlioglu
IPC分类号： H02S50/00 , G06N3/08 , H02S40/36
摘要： Various embodiments for a connection topology reconfiguration technique for photovoltaic (PV) arrays to maximize power output under partial shading and fault conditions using neural networks are disclosed herein.

10. 发明授权

US10387751B2 Methods, apparatuses, and systems for reconstruction-free image recognition from compressive sensors 有权
公开(公告)号：US10387751B2
公开(公告)日：2019-08-20
申请号：US15869780
申请日：2018-01-12
申请人： Henry Braun , Pavan Turaga , Andreas Spanias , Cihan Tepedelenlioglu
发明人： Henry Braun , Pavan Turaga , Andreas Spanias , Cihan Tepedelenlioglu
IPC分类号： G06K9/62 , G06N3/08 , G06N3/04 , G06K9/20 , G06K9/46
摘要： The disclosure relates to an image recognition algorithm implemented by a hardware control system which operates directly on data from a compressed sensing camera. A computationally expensive image reconstruction step can be avoided, allowing faster operation and reducing the computing requirements of the system. The method may implement an algorithm that can operate at speeds comparable to an equivalent approach operating on a conventional camera's output. In addition, at high compression ratios, the algorithm can outperform approaches in which an image is first reconstructed and then classified.

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