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.

Abstract: This disclosure relates to systems and methods for performing an autonomous procedure for monitoring and diagnostics of a machine using electrical signature analysis. In one embodiment of the disclosure, a method includes providing electrical data of an electrical rotating machine associated with at least one fault frequency. While in a learning mode, the method includes converting the electrical data from a time domain to a frequency domain to obtain baseline data. While in an operational mode, the method includes converting the electrical data from the time domain to the frequency domain to obtain monitoring data. The method further includes determining, based at least on the monitoring data, a ratio value at the fault frequency, determining a rate of change of the ratio value at the fault frequency, and, optionally, issuing, based on the rate of change, an alarm concerning at least one event of the electrical rotating 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.

Abstract: This disclosure relates to systems and methods for electrical signature analysis of electrical rotating machines. In one embodiment of the disclosure, a method includes ascertaining initial information associated with an electrical rotating machine, assigning a plurality of operational conditions associated with the machine to a plurality of buckets, and recording the electrical data to obtain a pre-defined number of sets of learning data. The method further includes determining, based at least on the initial information and the learning data, that the machine is in a healthy condition, obtaining, based on the learning data, baseline data associated with at the at least one bucket, and generating, based on the baseline data, a threshold associated with the bucket and at least one fault frequency associated with the machine. The method further includes generating, based on the threshold and the baseline data, alarms concerning a state of the electrical rotating machine.

Abstract: This disclosure relates to systems and methods for electrical signature analysis of electrical rotating machines. In one embodiment of the disclosure, a method includes ascertaining initial information associated with an electrical rotating machine, assigning a plurality of operational conditions associated with the machine to a plurality of buckets, and recording the electrical data to obtain a pre-defined number of sets of learning data. The method further includes determining, based at least on the initial information and the learning data, that the machine is in a healthy condition, obtaining, based on the learning data, baseline data associated with at the at least one bucket, and generating, based on the baseline data, a threshold associated with the bucket and at least one fault frequency associated with the machine. The method further includes generating, based on the threshold and the baseline data, alarms concerning a state of the electrical rotating machine.

Abstract: One or more embodiments of the disclosure pertain to a protection and diagnostic system that can generate a trip/alarm signal by executing a diagnostic procedure upon a machine that includes a rotating part. The diagnostic procedure can include using sensors to obtain performance parameters of various tagged critical and/or less critical sub-systems of the machine when the rotating part is rotating; determining, in real time, a system performance index of the machine based on the performance parameters; using a protection model to determine a sub-system performance index associated with at least one tagged sub-system of the machine; determining an asset health index based on combining the system performance index and the sub-system performance index; and generating the trip/alarm signal when the asset health index exceeds a threshold value. The protection system can include a protection device configured to receive the trip signal and execute a protective action upon the machine.

Abstract: This disclosure relates to systems and methods for performing an autonomous procedure for monitoring and diagnostics of a machine using electrical signature analysis. In one embodiment of the disclosure, a method includes providing electrical data of an electrical rotating machine associated with at least one fault frequency. While in a learning mode, the method includes converting the electrical data from a time domain to a frequency domain to obtain baseline data. While in an operational mode, the method includes converting the electrical data from the time domain to the frequency domain to obtain monitoring data. The method further includes determining, based at least on the monitoring data, a ratio value at the fault frequency, determining a rate of change of the ratio value at the fault frequency, and, optionally, issuing, based on the rate of change, an alarm concerning at least one event of the electrical rotating machine.

Abstract: One or more embodiments of the disclosure pertain to a protection and diagnostic system that can generate a trip/alarm signal by executing a diagnostic procedure upon a machine that includes a rotating part. The diagnostic procedure can include using sensors to obtain performance parameters of various tagged critical and/or less critical sub-systems of the machine when the rotating part is rotating; determining, in real time, a system performance index of the machine based on the performance parameters; using a protection model to determine a sub-system performance index associated with at least one tagged sub-system of the machine; determining an asset health index based on combining the system performance index and the sub-system performance index; and generating the trip/alarm signal when the asset health index exceeds a threshold value. The protection system can include a protection device configured to receive the trip signal and execute a protective action upon the machine.