Abstract: Methods, systems, and devices for automatically discovering and configuring intelligent electronic devices (IEDs) associated with power grids may determine, by a device adapter device, an address of an IED associated with a power substation; authenticating the IED to the device adapter device based on the address; receive, by a device adapter device, based on the authentication, device identifying information from an IED; provide, by the device adapter device, the device identifying information to an IED management system; identify, by the IED management system, based on the device identifying information, a firmware version currently used by the IED; identify, by the IED management system, based on the firmware version, additional device data of the IED; and update, by the IED management system, based on the additional data, a file of the IED.

Abstract: Provided herein is a power grid system comprising a plurality of transmission zones. Each of the plurality of transmission zones may include zonal measurement devices configured to measure zonal operational parameters for an assigned transmission zone of the plurality of transmission zones. Each of the plurality of transmission zones further includes a zonal controller associated with the assigned transmission zone and in communication with the zonal measurement devices. The zonal controller is configured to receive zonal operational data for the assigned transmission zone from the zonal measurement devices. The zonal controller is further configured to determine a zonal orchestration index based on the zonal operational data. In addition, the zonal controller is configured to determine an adaptive control action for a control device in the assigned transmission zone based on the zonal orchestration index and communicate a command to the control device based on the adaptive control action.

Abstract: A power grid system that includes a plurality of distribution zones is provided. An assigned distribution has a distribution zonal controller. The distribution zonal controller divides the assigned distribution zone into a plurality of sub-zones based on one or more assets. The distribution zonal controller also divides at least one of the plurality of sub-zones into a plurality of clusters based on one or more assets. The distribution zonal controller further receives sub-zonal operational data for the plurality of sub-zones from sub-zonal measurement devices and receives cluster operational data for each of the plurality of clusters from cluster measurement devices. A distribution zonal orchestration index is determined for the assigned distribution zone based on the sub-zonal operational data and the cluster operational data and adaptive control action is determined for at least one of the sub-zonal control devices or the cluster control devices based on the distribution zonal orchestration index.

Abstract: A method of operating a power grid comprising a plurality of transmission zones is provided herein. In some embodiments, the method includes receiving zonal operational data and receiving zonal forecast data from a plurality of zonal controllers associated with the plurality of transmission zones. The method also includes determining a zonal adaptive policy for a transmission zone of the plurality of transmission zones based on the zonal operational data and the zonal forecast data. The zonal adaptive policy defines a rule for a zonal controller associated with the transmission zone in a normal mode of operation. The method further includes communicating the zonal adaptive policy to at least one of the plurality of zonal controllers and communicating an intervention command to the zonal controller associated with the transmission zone. The intervention command intervenes with the normal mode of operation and adjusts an operational parameter of the transmission zone.

Abstract: A power grid system comprising an asset management controller associated with one or more of a plurality of zones in a power grid is provided herein. The asset management controller is configured to receive primary asset data for a primary asset associated with a zone of the plurality of zones. The asset management controller is further configured to receive secondary asset data for a secondary asset associated with the zone. The asset management controller is also configured to determine, via a machine learning model, a zonal analytics parameter. In addition, the asset management controller is configured to identify an asset management control action for a control device associated with the zone based on the zonal analytics parameter and communicate a command to a distribution zonal controller associated with the zone based on the asset management control action.

Abstract: A power grid system provided herein. The power grid system includes an assigned distribution zone of a plurality of distribution zones. The assigned distribution zone is divided into a plurality of sub-zones. Each of the plurality of sub-zones includes a sub-zonal measurement device and a sub-zonal control device. A distribution zonal controller is associated with the assigned distribution zone and IS in communication with the sub-zonal measurement devices and the sub-zonal control devices. The distribution zonal controller is configured to: receive sub-zonal operational data for each of the plurality of sub-zones from the sub-zonal measurement devices; determine a power restoration feasibility index for the assigned distribution zone based on the sub-zonal operational data; determine a restoration control action based on the power restoration feasibility index; and communicate a command to sub-zonal control devices based on the restoration control action.

Abstract: Devices, systems, and methods for platform capability description (PCD) file-based configuring of intelligent electronic devices (IEDs) may include generating, by at least one processor, PCD files for an IED, the PCD files defining the capabilities of the IED, a license of functions of the IED for a user, and rules applying to settings of the IED; generating, using an IED configurator tool from which IED rules are absent, based on the PCD files, a bundle of files for the IED, the bundle comprising a manifest and configuration files for the IED; and providing, by the IED configurator tool, to the IED, the bundle, the license of functions, and the rules, wherein the IED is configured based on the bundle, the license of functions, and the rules.

Abstract: A system for dynamic rating of a power grid may include a plurality of terminal units, and a controller. The terminal units may detect a voltage phasor and a current phasor at nodes of the power grid. The controller may, based on the voltage phasors and the current phasors of the plurality of nodes, determine a dynamic thermal stability power rating for each line, a dynamic angular stability power rating for each node, and a dynamic voltage stability power rating for each node. The controller may, based on the dynamic thermal stability power rating, the dynamic angular stability power rating, and the dynamic voltage stability power rating, determine a dynamic system rating for the power grid. The controller may control the power grid in response to the dynamic system rating.

Abstract: Systems and methods for virtualizing power substations may include virtual protection, automation, and control (VPAC) system including a first physical server including first virtual machines, the first virtual machines including a first virtual machine representing a first physical component of a power substation, a second virtual machine representing a backup of the first virtual machine, and a third virtual machine configured to evaluate a health of the first physical server; and a second physical server including second virtual machines, the second virtual machines including a fourth virtual machine representing a backup to the first virtual machine, a fifth virtual machine representing a backup to the second virtual machine, and a sixth virtual machine configured to evaluate a health of the second physical server.

Abstract: Systems and methods for virtualizing power substations may include generating, for a first physical device of a power substation, a first virtual machine that models characteristics of the first physical device; generating, based on forecasted weather and operational parameters of the power substation, settings for the first virtual machine; generating, based on physical sensor data for the power substation, virtual sensors; generating, based on virtual sensor data from the virtual sensors and the settings, an asset digital twin model of the first physical device; generating, based on the virtual sensor data and the asset digital twin model, a cyber digital twin for the first physical device; generating, based on the virtual sensor data and the asset digital twin model, a physics-based digital twin for the first physical device; and generating a substation digital twin virtually representing the power substation, including the cyber digital twin and the physics-based digital twin.

Abstract: Systems and methods are disclosed for asset health assessment and fleet management. An example method may include classifying a first power system asset into a first sub-system and a second sub-system. The example method may also include measuring, by a processor of a protection relay and from a first power system asset, electrical, thermal, and/or mechanical data associated with the first power system asset. The example method may also include identifying a first fault feature for the first sub-system, wherein the first fault feature is influenced by load oscillations in the first power system asset. The example method may also include comparing the first fault feature to a second fault feature of a third sub-system in a second power system asset, wherein the second fault feature is the same as the first fault feature, and wherein the second fault feature is not associated with load oscillations.

Abstract: Systems and methods for power system switching element (PSSE) anomaly detection are disclosed. An example PSSE anomaly detection unit may include a power system switching element position estimator (PSSEPE) and a comparison unit. The PSSEPE may be configured to receive a set of measurements and a set of control commands associated with a PSSE, calculate an anomaly confidence score based on the set of measurements and the set of control commands, and estimate a calculated PSSE position based on the set of measurements and the set of control commands. The comparison unit may be configured to receive the calculated PSSE position from the PSSEPE, receive the set of measurements and the set of control commands from the PSSEPE, receive a reported PSSE position associated with the PSSE, and determine a PSSE anomaly decision based on a difference between the reported PSSE position and the calculated PSSE position.

Abstract: A system for dynamic rating of a power grid may include a plurality of terminal units, and a controller. The terminal units may detect a voltage phasor and a current phasor at nodes of the power grid. The controller may, based on the voltage phasors and the current phasors of the plurality of nodes, determine a dynamic thermal stability power rating for each line, a dynamic angular stability power rating for each node, and a dynamic voltage stability power rating for each node. The controller may, based on the dynamic thermal stability power rating, the dynamic angular stability power rating, and the dynamic voltage stability power rating, determine a dynamic system rating for the power grid. The controller may control the power grid in response to the dynamic system rating.

Abstract: Systems and methods for power system switching element (PSSE) anomaly detection are disclosed. An example PSSE anomaly detection unit may include a power system switching element position estimator (PSSEPE) and a comparison unit. The PSSEPE may be configured to receive a set of measurements and a set of control commands associated with a PSSE, calculate an anomaly confidence score based on the set of measurements and the set of control commands, and estimate a calculated PSSE position based on the set of measurements and the set of control commands. The comparison unit may be configured to receive the calculated PSSE position from the PSSEPE, receive the set of measurements and the set of control commands from the PSSEPE, receive a reported PSSE position associated with the PSSE, and determine a PSSE anomaly decision based on a difference between the reported PSSE position and the calculated PSSE position.

Abstract: Systems and methods are disclosed for voltage-less electrical signature analysis for fault protection. The systems and methods described herein may involve determining voltage values for a motor (which may then be used to estimate a speed of the motor) when complete voltage measurements may not be available, or may only be temporarily available. More specifically, the systems and methods described herein may address three scenarios, which may include at least: (1) when only a single phase voltage input is available for a three-phase motor, (2) when no voltage input is available, or (3) when a voltage input is only available for a limited period of time (for example, during a learning phase of the motor).

Abstract: Systems and methods are disclosed for asset health assessment and fleet management. An example method may include classifying a first power system asset into a first sub-system and a second sub-system. The example method may also include measuring, by a processor of a protection relay and from a first power system asset, electrical, thermal, and/or mechanical data associated with the first power system asset. The example method may also include identifying a first fault feature for the first sub-system, wherein the first fault feature is influenced by load oscillations in the first power system asset. The example method may also include comparing the first fault feature to a second fault feature of a third sub-system in a second power system asset, wherein the second fault feature is the same as the first fault feature, and wherein the second fault feature is not associated with load oscillations.

Abstract: Systems, methods, and computer-readable media are disclosed for enhanced electrical signature analysis (ESA) for fault detection in electrical machines. The enhanced ESA uses an algorithm that is able to adaptively learn the behavior of a particular electrical machine and automatically establish fault thresholds for the electrical machine without requiring manual inputs from an operator. The particular algorithm described herein to accomplish this may use machine learning that may be used to model the behavior of the electrical machine in real-time and based on any properties specific to the electrical machine.

Abstract: Systems and methods are disclosed for voltage-less electrical signature analysis for fault protection. The systems and methods described herein may involve determining voltage values for a motor (which may then be used to estimate a speed of the motor) when complete voltage measurements may not be available, or may only be temporarily available. More specifically, the systems and methods described herein may address three scenarios, which may include at least: (1) when only a single phase voltage input is available for a three-phase motor, (2) when no voltage input is available, or (3) when a voltage input is only available for a limited period of time (for example, during a learning phase of the motor).

Abstract: Systems, methods, and computer-readable media are disclosed for enhanced electrical signature analysis (ESA) for fault detection in electrical machines. The enhanced ESA uses an algorithm that is able to adaptively learn the behavior of a particular electrical machine and automatically establish fault thresholds for the electrical machine without requiring manual inputs from an operator. The particular algorithm described herein to accomplish this may use machine learning that may be used to model the behavior of the electrical machine in real-time and based on any properties specific to the electrical machine.

Abstract: The technology described herein is generally directed towards a system for power transmission system protection and fault location, such as implemented in a deployable device at one or more junction points of a power transmission system. Aspects of the described technology can be directed to analyzing a traveling wave corresponding to a fault on a power transmission system. Example aspects can comprise receiving data representing current and voltage components of a traveling wave, maintaining the data in storage for fault location determination of the fault, transforming the data via a wavelet transform, into wavelet transform results and using the wavelet transform results for protection of the power transmission system.