LIOS in cooperation with its partner TechImp provide the world’s first combined Distributed Temperature Sensing (DTS) equipment together with Partial Discharge (PD) solutions to the power industry. This complemented combination of DTS and PD solutions provides grid owners an even better control and monitoring possibility of their grids.
Together with Real Time Thermal Rating (RTTR), a platform has been created for a complete scope of condition assessment, control, monitoring and ampacity predictions.
The Global Condition Monitoring approach allows most of the failure modes of apparatus and electrical assets to be diagnosed, thus increasing reliability and decreasing maintenance costs, thanks to phenomena correlation.
- Diagnostics allows failure uncertainty to be reduced.
- Optimization of the maintenance procedures
- Condition Based Maintenance
- Maximization of the system components availability.
The crucial benefit of this cooperation between two leading suppliers to the T&D market is characterised by the innovative concept of Global Condition Monitoring, based on general purpose platforms, supporting different sensors (PD, DGA, DTS, Tan-delta and Vibrations) through a unique system integrating diagnostic algorithms upon data coming from these sensors. Therefore an overall reinforcement of the diagnostic effectiveness and robustness of the diagnostic power is obtained, but at a lower cost.
Such a superior diagnostic power is achieved by advanced tools, formerly developed for PD, based on TechImp’s unique SID (Separation, Identification, Diagnosis) strategy, which allows noise rejection, PD source separation and identification. TechImp patented technology provides a powerful and efficient diagnostic approach able to disentangle even the most critical PD phenomena, thus improving PD identification from different overlapping PD and noise sources.
TechImp PD separation technology allows different Partial Discharge phenomena to be classified on the basis of their pulse shape and split in different clusters (TF map®), so that further analysis can be carried out on each dataset, separately. This enhances the likelihood of PD source identification, even for non skilled operators.
Now this technologies have been extended to integrate novel sensors and data, adding integrated diagnostic levels based on fuzzy logic and artificial intelligence techniques for a cross-correlation on the whole set of data, giving a better and global coverage of possible faults, comprising, electrical, chemical/physical, mechanical degradation of the monitored electrical asset.