Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics
Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously moni...
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2024-10-01
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| author | Joshua H. Tyler Donald R. Reising Thomas Cooke Anthony Murphy |
| author_facet | Joshua H. Tyler Donald R. Reising Thomas Cooke Anthony Murphy |
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| description | Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data’s storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.58</mn></mrow></semantics></math></inline-formula>%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2024-10-01 |
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| spelling | doaj-art-0d2f2d3d0cf541f294d41286d6ce99dc2024-11-08T14:33:56ZengMDPI AGApplied Sciences2076-34172024-10-011421995810.3390/app14219958Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault ForensicsJoshua H. Tyler0Donald R. Reising1Thomas Cooke2Anthony Murphy3Electrical Engineering, The University of Tennessee at Chattanooga, 735 Vine Street, Chattanooga, TN 37403, USAElectrical Engineering, The University of Tennessee at Chattanooga, 735 Vine Street, Chattanooga, TN 37403, USAElectrical Power Research Institute, Knoxville, TN 37932, USATennessee Valley Authority, Chattanooga, TN 37402, USAAcross the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data’s storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.58</mn></mrow></semantics></math></inline-formula>%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event.https://www.mdpi.com/2076-3417/14/21/9958incipient fault predictionpower gridsmart griddata compressionevent forecastingstate estimation |
| spellingShingle | Joshua H. Tyler Donald R. Reising Thomas Cooke Anthony Murphy Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics Applied Sciences incipient fault prediction power grid smart grid data compression event forecasting state estimation |
| title | Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics |
| title_full | Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics |
| title_fullStr | Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics |
| title_full_unstemmed | Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics |
| title_short | Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics |
| title_sort | power signal histograms a method of power grid data compression on the edge for real time incipient fault forensics |
| topic | incipient fault prediction power grid smart grid data compression event forecasting state estimation |
| url | https://www.mdpi.com/2076-3417/14/21/9958 |
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