Abstract: Systems and methods for meter-to-transformer connectivity. The system can include a data processing system comprising one or more processors coupled with memory. The data processing system can receive a time-series data set of voltage measured by meters at a plurality of loads in an electricity distribution grid. The data processing system can construct, based on the time-series data set, a matrix with values that indicate similarities between pairs of meters of the meters. The data processing system can generate, via clustering techniques applied to the matrix, a silhouette score for each of the meters in the matrix to group the meters into transformer groups. The data processing system can provide, for output via a graphical user interface, a digital map with an indication of a subset of meters of the meters associated with a transformer of the transformer groups generated based on the clustering techniques applied to the matrix.

Abstract: Systems and methods for a smart grid distributed operations platform. The system can include a data processing system comprising one or more processors, coupled with memory. The data processing system can identify a network connection with edge devices located on an electricity distribution grid, wherein each of the edge devices comprise a processor and memory. The data processing system can identify, based on a configuration associated with the edge devices, an application configured to perform a function to manage delivery of electricity via the electricity distribution grid. The data processing system can select a model from models trained with machine learning based on a local environment attribute associated with a location on the electricity distribution grid at which the edge devices are located. The data processing system can provide, for execution on the edge devices, the application configured with the model to perform the function to manage delivery of electricity.

Abstract: Meter voltage fingerprint is provided. A system can include a metering system at a location downstream from a substation on a utility grid that distributes electricity. The metering system can receive data samples of a voltage waveform corresponding to the electricity distributed at the location. The metering system can determine first metrics for the voltage waveform over a time interval via a statistical technique. The metering system can determine second metrics for the voltage waveform over the time interval based on a difference between the voltage waveform and a model waveform. The metering system can construct a data structure comprising the first metrics, the second metrics, and an identifier for the location. The metering system can provide the data structure to a data processing system remote from the metering system to cause the data processing system to evaluate a performance of the utility grid.

Abstract: Anomaly detection, classification, and prediction is provided. A system can include one or more processors coupled with memory. The system can identify voltage waveform data corresponding to electricity distributed over a utility grid and measured by a metering device. The system can detect, based on a comparison with baseline voltage waveform data, an anomaly in at least a portion of the voltage waveform data. The system can generate spectrogram data for the at least the portion of the voltage waveform data comprising the anomaly. The system can determine, via a model trained with machine learning, a type of the anomaly based on the spectrogram data. The system can provide an indication of the type of the anomaly to cause an action to be performed on the utility grid responsive to determination of the type of anomaly.

Abstract: Systems and methods for real-time (EV) charging detection. The system can include a metering system. The metering system can detect, via a sensor, current associated with electricity consumed in the utility grid during a first time interval. The metering system can determine a current harmonic metric and a power metric associated with the electricity delivered over the utility grid in the first time interval. The metering system can input the current harmonic metric and the power metric into a model trained with machine learning and deployed on the metering system to determine a likelihood that at least a portion of the electricity delivered over the utility grid in the first time interval is used to charge an electric vehicle. The metering system can execute an action associated with performance of the utility grid responsive to the likelihood satisfying a threshold.

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.