Abstract: Systems and methods comprising a metering device located on an electricity distribution grid, the metering device comprising one or more processors and memory. The metering device can detect a drop in characteristic of electricity below a threshold indicating a fault on the electricity distribution grid. The metering device can generate, responsive to the drop in the characteristic of electricity below the threshold, a time series of a rate of change of the characteristic of electricity for a predetermined number of cycles subsequent to the detection of the drop. The metering device can determine, based on a comparison of the time series of the rate of change with a predetermined pattern, a location of the metering device on the electricity distribution grid relative to a location of the fault on the electricity distribution grid.

Abstract: Systems and methods comprising a metering device located on an electricity distribution grid, the metering device comprising one or more processors and memory. The metering device can detect a drop in characteristic of electricity below a threshold indicating a fault on the electricity distribution grid. The metering device can generate, responsive to the drop in the characteristic of electricity below the threshold, a time series of a rate of change of the characteristic of electricity for a predetermined number of cycles subsequent to the detection of the drop. The metering device can determine, based on a comparison of the time series of the rate of change with a predetermined pattern, a location of the metering device on the electricity distribution grid relative to a location of the fault on the electricity distribution grid.

Abstract: Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.

Abstract: Systems and methods are directed to controlling components of a utility grid. The system can receive data samples including signals detected at one or more portions of a utility grid. The system can construct a matrix having a first dimension and a second dimension. The system can train a machine learning model based on the matrix to predict values for signals of the utility grid not provided in the matrix. The system can receive bounds for one or more input variables, constraints on one or more output variables, and a performance objective for the utility grid. The system can determine, based on the machine learning model and via an optimization technique, an adjustment to a component of the utility grid that satisfies the performance objective. The system can provide the adjustment to the component of the utility grid to satisfy the performance obj ective.

Abstract: Systems and methods comprising a metering device located on an electricity distribution grid, the metering device comprising one or more processors and memory. The metering device can detect a drop in characteristic of electricity below a threshold indicating a fault on the electricity distribution grid. The metering device can generate, responsive to the drop in the characteristic of electricity below the threshold, a time series of a rate of change of the characteristic of electricity for a predetermined number of cycles subsequent to the detection of the drop. The metering device can determine, based on a comparison of the time series of the rate of change with a predetermined pattern, a location of the metering device on the electricity distribution grid relative to a location of the fault on the electricity distribution grid.

Abstract: Systems and methods are directed to controlling components of a utility grid. The system can receive data samples including signals detected at one or more portions of a utility grid. The system can construct a matrix having a first dimension and a second dimension. The system can train a machine learning model based on the matrix to predict values for signals of the utility grid not provided in the matrix. The system can receive bounds for one or more input variables, constraints on one or more output variables, and a performance objective for the utility grid. The system can determine, based on the machine learning model and via an optimization technique, an adjustment to a component of the utility grid that satisfies the performance objective. The system can provide the adjustment to the component of the utility grid to satisfy the performance objective.

Abstract: Systems and methods comprising a metering device located on an electricity distribution grid, the metering device comprising one or more processors and memory. The metering device can detect a drop in characteristic of electricity below a threshold indicating a fault on the electricity distribution grid. The metering device can generate, responsive to the drop in the characteristic of electricity below the threshold, a time series of a rate of change of the characteristic of electricity for a predetermined number of cycles subsequent to the detection of the drop. The metering device can determine, based on a comparison of the time series of the rate of change with a predetermined pattern, a location of the metering device on the electricity distribution grid relative to a location of the fault on the electricity distribution grid.

Abstract: Systems and methods are directed to controlling components of a utility grid. The system can receive data samples including signals detected at one or more portions of a utility grid. The system can construct a matrix having a first dimension and a second dimension. The system can train a machine learning model based on the matrix to predict values for signals of the utility grid not provided in the matrix. The system can receive bounds for one or more input variables, constraints on one or more output variables, and a performance objective for the utility grid. The system can determine, based on the machine learning model and via an optimization technique, an adjustment to a component of the utility grid that satisfies the performance objective. The system can provide the adjustment to the component of the utility grid to satisfy the performance objective.

Abstract: Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.

Abstract: Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.

Abstract: Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.

Abstract: Systems and methods of the present disclosure are directed to determining the integrity of system measurements. The system and methods of the present disclosure can detect corrupted process measurement signals that can originate in instrumentation used with electricity distribution and transmission systems. These process measurement signals may be delivered by digital communications networks.

Abstract: Systems and methods of detecting an attack in a utility grid are described. An anomaly detector identifies a first indication of signal samples used by a controller to adjust a voltage level at a load terminal during a first time interval, and a first indication of voltage levels at the load terminal of the controller. The anomaly detector identifies a regulator emulation model (“REM”) for the controller. The anomaly detector receives a second indication of signal samples used by the controller during a second time interval, and a second indication of a voltage level at the load terminal. The anomaly detector detects a level of conformance with the REM based on a comparison of the second indication of the voltage levels with a voltage level determined by inputting the second indication of the signal samples into the REM. The anomaly detector provides a notification indicating an anomaly with the controller.

Abstract: A method is disclosed of controlling the operation of a system for providing electrical power to one or more electrical devices, the system comprising an adjustable power source root node and a plurality of adjustable power source remote nodes located remotely from the root node.

Abstract: This disclosure is directed to a system and method of allocating energy provided by a power source. The system includes a computing device that receives, from meters, observations of energy delivered by the power source to sites. The device classifies the sites into consumption classes, where a first consumption class has complete coverage and a second consumption class has incomplete coverage. The device determines a metric for a characteristic of energy for the first class. The device determines a first demand for the first class based on the metric for the characteristic of energy for the first class. The device generates a sampling distribution of the metric for the characteristic for the second class and determines a second demand for the second class based on the sampling distribution. The device determines a dissipation metric based on the first and second demands.

Abstract: The present disclosure is directed to providing voltage via a power distribution system. A computing device receives delivered voltage information from metering devices metering power distributed to sites by a controller. The computing device determines a number of metering devices to use to generate a control signal to control operation of the controller. The number can be determined based on the delivered voltage information for each site. The computing device selects, based on the delivered voltage information, at least the determined number of metering devices to form a subset of metering devices for a subset of sites. The computing device uses the delivered voltage information of the subset of metering devices to generate the control signal. The control signal can control operation of the at least one controller distributing power to the plurality of sites.

Abstract: A mesh delivery system for controlling electricity supplied to one or more electrical devices from a power source are disclosed. Signals relating to characteristics of the electrical devices are uniformly or non-uniformly sampled at discrete intervals are provided along with associated sampling time indications via the mesh network to a destination controller. The controller applies a compacting algorithm to the received samples and the received associated time indications to generate a paired vector comprising samples and an associated compacted time indication. The paired vector representing the sampled signal in a time domain is transformed into a discrete Fourier spectrum of the signal in the frequency domain where the discrete Fourier spectrum of the signal is in a uniformly sampled in the frequency domain.

Abstract: This disclosure is directed to regulating electric power at a node of a system for distribution of electricity. A voltage controller can identify properties of branch structures in a system that includes a voltage regulation device that controls a voltage source supplying electricity to nodes via the branch structures. The voltage controller can receive information on voltage and current associated with electricity provided by the voltage source. The voltage controller can receive, from a metering devices at nodes in the system, primary voltage information. The voltage controller can select one of the nodes based on the primary voltage information. The voltage controller can determine, based on the properties, an impedance for a branch structure corresponding to the selected node. The voltage controller can control the voltage regulation device based on the impedance for the branch structure corresponding to the selected node and the information on the voltage and the current.

Abstract: A method and apparatus for controlling electric power supplied to one or more electrical devices from a power source are disclosed. Measurements of the supplied electricity are detected. Estimated deviant voltage levels that the supplied electricity will not drop below or exceed as a result of varying electrical consumption by the one or more electrical devices is computed based on a predetermined confidence level and the detected measurements. A voltage level output of the electricity supplied to the electrical device is adjusted based on the computed deviant voltage level.

Abstract: A method is disclosed including receiving with a controller at a destination node signal samples and associated sampling time indications. The signal samples and the associated sampling time indications are received from a source node via a mesh network. The signal samples are delivered with sampling time indications generated at the source node to form a series of signals corresponding to one or more characteristic(s) related to electricity supplied to one or more electrical devices from a power source. The method also includes applying a time domain convolution procedure to the received signal in the time domain that is uniformly sampled. The weighting of sample values in time domain convolution procedure is determined at least partially based on information indicative of the statistical behavior of a corresponding realized sample process.