Abstract: 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.

Abstract: A system reconfigures a photovoltaic array used in solar energy based on observed shading conditions to determine an optimal topology of the photovoltaic array to maximize power output. Specifically, the system is designed to reconfigure a photovoltaic array when the photovoltaic array is partially shaded. The system uses a neural network model to determine a topology that maximizes power output of the photovoltaic array based on irradiance data obtained from the photovoltaic array.

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

Abstract: 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.

Abstract: 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.

Abstract: 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.

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

Abstract: 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.

Abstract: 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.

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

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

Abstract: 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.

Abstract: Solar array fault detection, classification, and localization using deep neural nets is provided. Embodiments use 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 at 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.

Abstract: 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.

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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.