Abstract: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: Example systems, methods, and non-transitory computer readable media are directed to obtaining a power forecast to be provided to a grid operator of an electrical network, wherein the power forecast estimates an output of energy from power generation resources; determining a likely deviation from a nominal grid frequency during a forecast period indicating a likely imbalance penalty; determining whether to achieve a likely reduction in the imbalance penalty by (a) adjusting the power forecast or (b) controlling a battery energy storage system (BESS) associated with the power generation resources; in response to determining to achieve the likely reduction in the imbalance penalty by adjusting the power forecast: determining an adjusted power forecast; and providing the adjusted power forecast to the grid operator; and in response to determining to achieve the reduction in the imbalance penalty by controlling the BESS associated with the power generation resources: causing an adjustment to a charge rate or disc

Abstract: An apparatus for estimating a condition of a battery includes a mode identifying unit configured to identify a usage mode of the battery during a period of time and its corresponding attenuation curve, according to recorded data on battery usage, stored usage modes of the battery and attenuation curves corresponding to the various usage modes, the attenuation curve representing a change of a fully charged capacity of the battery with battery usage; and a condition estimating unit configured to calculate battery degradation according to the recorded data, the identified usage mode and its corresponding attenuation curve, the degradation representing a quantity of the fully charged capacity of the battery that is reduced over the battery usage. The condition of the battery is estimated so as to rationally judge the residual value of the battery in operation.

Abstract: An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.

Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

Abstract: A system and method to schedule inspections for a plurality of assets at a corresponding plurality of outdoor locations are described. The method includes retrieving location attributes of each of the plurality of assets, retrieving terrestrial geotagged images that include one or more of the plurality of assets, obtaining parameters associated with each of the plurality of assets based on information from the images, and determining risk factors associated with each of the plurality of assets based on the associated parameters and a failure mapping. The method also includes determining a risk score for each of the plurality of assets based on the corresponding risk factors, and scheduling inspections of the plurality of assets based on the risk score associated with each of the plurality of assets.

Abstract: A system and method of managing a network that includes assets are described. The method includes modeling the network as a directed graph with each of the assets represented as a node and determining alternative paths to each node from each available corresponding source of the node. The method also includes computing upstream robustness of each node, computing upstream robustness of the network, and computing downstream criticality of each node. Managing the network and each asset of the network is based on the upstream robustness and the downstream criticality of each node.

Abstract: Example systems, methods, and non-transitory computer readable media are directed to determining a geographic site to be assessed for lightning activity over a period of time; obtaining weather information associated with the geographic site, the weather information being generated by a weather prediction model for the period of time, wherein the weather information comprises a grid-based representation of a weather map; determining one or more grid points to assess the geographic site for lightning activity; evaluating weather-related metrics associated with the one or more grid points; determining one or more likelihoods of the geographic site experiencing lightning activity during the period of time based at least in part on the evaluation; and providing weather forecast information for the geographic site, the weather forecast information including at least the one or more likelihoods of the geographic site experiencing lightning activity during the period of time.

Abstract: Various embodiments manage energy generation in a power generation and distribution system. In one embodiment, a set of residual load data is obtained for a given period of time measured at one or more nodes within a power generation and distribution system. The set of residual load data encodes a set of power flow signals. The set of residual load data is analyzed. An amount of power contributed to the set of residual load data by at least one energy generator class is determined based on the analysis of the set of residual load data.

Abstract: An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.

Abstract: An example method includes receiving time-series vibration data for multiple components of multiple wind turbines. Based on the time-series vibration data, multiple groups of wind turbines are defined, as well as first and second vibration thresholds for each group. Time-series vibration data for a component of a particular wind turbine is received. A particular group that includes the particular wind turbine, as well as particular first and second vibration thresholds are identified. A vibration of the component has exceeded the particular first vibration threshold is determined. A forecast of at least one of a period of time before the vibration may exceed the particular second vibration threshold and a future date at which the vibration may exceed the particular second vibration threshold is generated. An alert that includes the particular wind turbine and at least one of the period of time and the future date is generated and provided.

Abstract: A method, system, and computer program product to manage a network comprising a plurality of interconnected components are described. The method includes obtaining a set of all the components that are part of the network over time, and identifying one or more repeating patterns of components among the set of all the components as corresponding lower-level definitions to generate a hierarchical set of all the components. The method also includes obtaining time-varying information regarding topology and operational values within the network, and creating a representation of the network at a set of times based on the hierarchical set of all the components and the time-varying information.

Abstract: An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.

Abstract: An example method comprises receiving sensor data from multiple wind turbines. A wind turbine includes a gearbox, a generator, and multiple gearbox and generator subcomponents. Health indicators may be determined for the gearbox and generator subcomponents with varying lead times. The health indicators correspond to alerts for current or predicted problems of the gearbox and generator subcomponents and have either low severity, medium severity, or high severity risk levels. A machine learning model trained on sensor data may generate the alerts. The multiple wind turbines may be displayed in a list that may be sortable by health indicators for the gearbox subcomponents and the generator subcomponents. The list may be filterable by alerts for the gearbox subcomponents or the generator subcomponents.

Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

Abstract: An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

Abstract: A method for creating a spatio-temporal schedule includes receiving a set of available work crews and a set of workorders to be scheduled, sequencing the set of workorders, wherein each workorder of the set of workorders is associated with a geographic location, a start time and an end time, and iterating through the set of available work crews, assigning a plurality of workorders of the set of workorders fully allocating a current crew before selecting a next crew, until all work crews are fully allocated and/or all workorders are assigned, wherein the assignment of workorders for a given work crew generates a route comprising the plurality of workorders.

Abstract: An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.

Abstract: The present invention relates to a method and apparatus for controlling a wind turbine. The method includes: dividing a plurality of wind turbines into at least one group based on a similarity in status information of the plurality of wind turbines; in response to having detected a fault in a first wind turbine of the plurality of wind turbines, searching a group to which the first wind turbine belongs for a second wind turbine matching status information of the first wind turbine; and controlling the first wind turbine based on parameters from the second wind turbine.

Abstract: A method for creating a spatio-temporal schedule includes receiving a set of available work crews and a set of workorders to be scheduled, sequencing the set of workorders, wherein each workorder of the set of workorders is associated with a geographic location, a start time and an end time, and iterating through the set of available work crews, assigning a plurality of workorders of the set of workorders fully allocating a current crew before selecting a next crew, until all work crews are fully allocated and/or all workorders are assigned, wherein the assignment of workorders for a given work crew generates a route comprising the plurality of workorders.