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1. 发明申请

US20230139907A1 SYSTEMS AND METHODS FOR IMPROVED FAULT DIAGNOSTICS OF ELECTRICAL MACHINES UNDER DYNAMIC LOAD OSCILLATIONS 有权
公开(公告)号：US20230139907A1
公开(公告)日：2023-05-04
申请号：US17452838
申请日：2021-10-29
申请人： General Electric Technology GmbH
发明人： Ali SHAHID , Sumitha MOHAN , Rupam MUKHERJEE , Arvind Kumar TIWARI , Balakrishna PAMULAPARTHY
IPC分类号： H02P29/028 , G01R31/34
摘要： 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.

2. 发明公开

US20230221693A1 SYSTEMS AND METHODS FOR INTEGRATED CONDITION MONITORING FOR POWER SYSTEM ASSET HEALTH SCORING 审中-公开
公开(公告)号：US20230221693A1
公开(公告)日：2023-07-13
申请号：US17666237
申请日：2022-02-07
申请人： General Electric Technology GmbH
发明人： Balakrishna PAMULAPARTHY , Mitalkumar KANABAR , Austin BYRNE , Umar Naseem KHAN
IPC分类号： G05B19/042
CPC分类号： G05B19/0428 , G05B2219/2639
摘要： 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.

3. 发明申请

US20220246378A1 ENHANCED SWITCHGEAR MONITORING AND DIAGNOSTICS IN A PROTECTION RELAY 有权
公开(公告)号：US20220246378A1
公开(公告)日：2022-08-04
申请号：US17164804
申请日：2021-02-01
申请人： General Electric Technology GmbH
发明人： Balakrishna PAMULAPARTHY , Palak Praduman PARIKH , Michael PILON
IPC分类号： H01H47/00 , G01R31/327
摘要： Systems, methods, and computer-readable media are disclosed 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.

4. 发明公开

US20230300170A1 SYSTEMS AND METHODS FOR POWER SYSTEM SWITCHING ELEMENT ANOMALY DETECTION 审中-公开
公开(公告)号：US20230300170A1
公开(公告)日：2023-09-21
申请号：US17696164
申请日：2022-03-16
申请人： General Electric Technology GmbH
发明人： Masoud ABBASZADEH , Mitalkumar KANABAR , Subhrajit ROYCHOWDHURY , Pubudu Eroshan WEERATHUNGA , Balakrishna PAMULAPARTHY
IPC分类号： H04L9/40 , H04L41/16 , G06F1/3206
CPC分类号： H04L63/1466 , H04L63/20 , H04L41/16 , G06F1/3206
摘要： 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.

5. 发明申请

US20220291285A1 ENHANCED ELECTRICAL SIGNATURE ANALYSIS FOR FAULT DETECTION 有权
公开(公告)号：US20220291285A1
公开(公告)日：2022-09-15
申请号：US17201563
申请日：2021-03-15
申请人： GENERAL ELECTRIC TECHNOLOGY GMBH
发明人： Balakrishna PAMULAPARTHY , Mitalkumar KANABAR , Akilezkrishnamurthy ARTHANARI
IPC分类号： G01R31/34 , G01N25/72 , G06N20/00
摘要： 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.

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