A low-complexity method based on compressed sensing for long term field measurement of insulator leakage current


Field measurements of insulator leakage current are necessary for long run knowledge storage for analysis purposes and condition-based mostly maintenance. Long term monitoring and storage of actual waveform are required since the electrical activity on the insulator surface can't be predicted before. This results in the accumulation of large amount of data. One solution is to optimize the native space for storing usage by implementing mathematical tools to eliminate noise and data with low data content. But, the strategy is computationally intensive and massive scale deployment of devices primarily based on such methods is costly. Proper condition-based mostly maintenance can't be guaranteed without large-scale use of monitoring devices in the field. Therefore, the authors propose the utilization of an alternate low-complexity approach primarily based on compressed sensing that addresses the problem of information size in field measurements. The compressed sensing primarily based technique is perfectly appropriate for massive-scale deployment of leakage current measurement devices within the context of field monitoring in insulators. The tactic is structured to deploy low computational burden in the monitoring devices by shifting the whole computational load from the monitoring device in the sector to the decoder at the central monitoring station. Completely different classes of field measured waveforms are used to demonstrate the performance of the proposed methodology. It's expected that this paper can contribute to the event of economical field monitoring devices in insulators.

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