Arizona State University

Cihan Tepedelenlioglu

Andreas Spanias

Pavan Turaga

Mahesh K Banavar

Gowtham Muniraju

Sameeksha Katoch

Xue Zhang

Rajapandian Ayyanar

Vivek Sivaraman Narayanaswamy

Robert Santucci

Sunil Srinivasa Manjanbail Rao

Sai Zhang

Sunil Rao

Henry Braun

Jie Fan

Various embodiments of a system and associated method for detecting and classifying faults in a photovoltaic array using graph-based signal processing.

Systems and methods for fault classification in photovoltaic arrays using graph signal processing

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.

Systems and methods for consensus-based distributed graph spectral radius estimation

Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Systems and methods for skyline prediction for cyber-physical photovoltaic array control

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.

Solar array fault detection, classification, and localization using deep neural nets

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.

Systems and methods for connection topology optimization in photovoltaic arrays using neural networks

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.

Systems and methods for robust max consensus for wireless sensor networks

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.

Distributed network center and area estimation

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.

Methods, apparatuses, and systems for reconstruction-free image recognition from compressive sensors

The wireless sensor network can including a plurality of anchor sensors each including a signal receiver, a processing module, and a transmitter. The signal receiver can be configured to detect a received signal. The received signal can include noise and further can include a transmitted signal from the node when the node is transmitting the transmitted signal. The node can be located at an exact location that is unknown to each of the plurality of anchor sensors. The exact location can be in a region that is known to each of the plurality of anchor sensors. The transmitted signal can be wirelessly transmitted from the node when the node is transmitting the transmitted signal. The wireless sensor network also can include a fusion center. The processing module of each anchor sensor of the plurality of anchor sensors can performs acts. The acts can include determining a probability of whether the node is present at each of a plurality of grid points of the region. The acts also can include determining a local detection determination of whether the node is detected by the anchor sensor based at least in part on the probabilities of whether the node is present at the plurality of grid points. The transmitter of each anchor sensor of the plurality of anchor sensors can send the local detection determination from the anchor sensor to the fusion center. Other embodiments are disclosed.

Distributed location detection in wireless sensor networks

Some embodiments include a wireless sensor network system. Other embodiments of related systems and methods are also disclosed.

Maximum likelihood localization in the presence of channel uncertainties

Some embodiments include a distributed estimation system. Other embodiments of related systems and methods are also disclosed.

Growing community of inventors

Chandler, AZ, United States of America

Cihan Tepedelenlioglu