General Electric Company

Ge Infrastructure Technology, LLC

General Electric Technology Gmbh

Ge Vernova Infrastructure Technology LLC

Balakrishna Pamulaparthy

Mitalkumar G Kanabar

Vijayasarathi Muthukrishnan

Balamourougan Vinayagam

Sumitha Mohan

Prabhakar Neti

Mitalkumar Kanabar

Akilezkrishnamurthy Arthanari

Ali Shahid

Sudhanshu Mishra

George Paul Gerdan

Palak Praduman Parikh

Masoud Abbaszadeh

Subhrajit Roychowdhury

Shahid Ali

Michael Shane Pilon

Rajagopal Kommu

Lubomir Hristov Sevov

Sridhar Nedunsezian

Manish Kumar Sharma

Aditya Gupta

Madu Thirugnanasam Moorthy

Anuradha Charugalla

Kvs Manoj Kumar Vadali

Sergio Dominguez Ruiz

Viswesh Goli

Raju Gurrapu

Manishkumar Ramchandra Sharma

Pubudu Eroshan Weerathunga

Austin Byrne

Shailender Singireddy

Umar Naseem Khan

Arvind Kumar Tiwari

Rupam Mukherjee

Lubomir Sevov

Madu Thirugnansam Moorthy

The present application provides a system for malicious control detection in power grids. The system includes at least one node configured to detect power grid parameters for each power phase and generate a signal indicative of time-series sensor measurements for each power phase. A controller in communication with the node may be configured to receive from the at least one node, the respective signals, extract at least one feature from the respective signals, provide the at least one feature as an input to a deep-learning model, receive an output from the deep-learning model indicative of a relationship between the power grid parameters and a node health associated with the at least one node, generate a status tag associated with the at least one node based at least in part on the output, wherein the status tag is normal or malicious, and generate a status signal indicative of the status tag.

Systems and methods for malicious control detection in a power grid

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. The example method may also include adjusting a threshold value based on the comparison of the first fault feature to the second fault feature of the third sub-system. The example method may also include calculating, by the processor and for a first sub-system of the first power system asset, a first value based on the electrical, thermal, and/or mechanical data. The example method may also include calculating, by the processor, based on the first value, and using recent measurement data, a second value associated with the first sub-system. The example method may also include calculating, by the processor and using historical average data, a third value associated with the first sub-system. The example method may also include determining, by the processor and based on the second value and the third value, a fourth value associated with the first power system asset. The example method may also include determining, by the processor, that the fourth value is greater than a threshold value. The example method may also include generating, by the processor, a warning based on the determination that the fourth value is greater than the threshold value.

Systems and methods for integrated condition monitoring for power system asset health scoring

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.

Systems and methods for power system switching element anomaly detection

The present disclosure relates to systems and methods for improved anomaly detection for rotating machines. An example method may include determining a rotational speed of a rotating machine; determining, using a frequency domain transform of a signal, a frequency domain signal; determining, based on the rotational speed, a first frequency band within the frequency domain signal for identifying a fault frequency; determining a fault frequency within the first frequency band; determining, based on the fault frequency, a second frequency band within the first frequency band, wherein the second frequency band includes the fault frequency; determining, based on the second frequency band, a first fault index and a baseline of the first fault index; determining, based on a deviation of a second fault index from the baseline, a fault condition; and providing an alert based on the fault condition.

Anomaly detection for rotating machines

Systems and methods are disclosed for improved fault diagnostics of electrical machines under dynamic load oscillations. The systems and methods may rely on one or more different algorithms for performing such fault diagnostics. One example, algorithm may involve determining a ratio of an instantaneous real power and a reactive power of the motor.

Systems and methods for improved fault diagnostics of electrical machines under dynamic load oscillations

Systems, methods, and computer-readable media are used for monitoring and diagnosing power system assets. An example of a power system asset may include an individual circuit breaker, a switchgear that may include multiple circuit breakers, or any other asset that may be included in a power system. A system for monitoring and diagnosing these power system assets may include one or more intelligent protection relay and switchgear monitor device(s) that may be communicatively coupled in a master-slave or peer-peer configuration in a time-synchronized manner of operation.

Enhanced switchgear monitoring and diagnostics in a protection relay

Systems, methods, and computer-readable media are disclosed for monitoring and diagnosing power system assets. An example method may include triggering, by a gateway device and at a first time, a capture of a first waveform from a first intelligent electronic device (IED) associated with a first asset in a power system. The method may also include transmitting, by the gateway device, the capture of the waveform to a remote device. The method may also include extracting fault features from the first waveform corresponding to different failure modes associated with the asset of the power system. The method may also include determining, based on the features extracted from first waveform, that a fault of a first fault mode has occurred in the asset. The method may also include providing an alert that the fault has been identified, wherein the alert initiates or otherwise facilitates a control action in the power system.

Systems and methods for monitoring and diagnosing power system assets

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).

Systems and methods for providing voltage-less electrical signature analysis for fault protection

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.

Enhanced electrical signature analysis for fault detection

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.

Autonomous procedure for monitoring and diagnostics of machine based on electrical signature analysis

Embodiments of the disclosure relate to systems and methods for monitoring and diagnosing transformer health. In one embodiment, a system incorporating a diagnostic apparatus for monitoring a power transformer can be provided. Various electrical current sensing elements and a dissolved gas analysis (DGA) apparatus are coupled to the transformer and to the diagnostic apparatus. The diagnostic apparatus can be configured to detect a through-fault in the transformer by executing an electrical current flow analysis based at least in part on electrical current values received from the electrical current sensing elements. The electrical current flow analysis involves comparing a ratio of a differential electrical current value and a restraining electrical current value to a threshold value. The diagnostic apparatus can also use DGA data provided by the DGA apparatus to detect an abnormal gas-related condition in the transformer. A transformer health related signal and/or a control signal that is based on the transformer health data, can be transmitted by the diagnostic apparatus.

Systems and methods for monitoring and diagnosing transformer health

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.

Electrical signature analysis of electrical rotating machines

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.

Machine protection and diagnostic systems and methods

A system, in one embodiment, includes a voltage fault detection system. The voltage fault detection system may be configured to acquire a reference voltage signal from a power line to determine if a voltage sag condition is present in the power line, determine a correction voltage for correcting the voltage sag condition, use the reference voltage to produce the correction voltage, and apply the correction voltage to the power line.

Dynamic voltage restoration system and method

Described herein are embodiments of methods, devices and systems of phase functionality for a meter. One aspect comprises a method of phase functionality for a meter. One embodiment of the method comprises determining a load value for each phase of a poly-phase electrical system; comparing, using a processor, the load value for each phase of a poly-phase electrical system with a respective threshold value for each phase of the poly-phase electrical system; and opening or closing one or more relays based on the comparison, wherein the one or more relays are each associated with respective phase of the poly-phase electrical system.

Growing community of inventors

Andhra Pradesh, India

Balakrishna Pamulaparthy