Delta-X Research Fully Releases Reliability-Based Dissolved Gas Analysis

Delta-X Research has fully released its innovative technology called Reliability-based Dissolved Gas Analysis.

The technology is a new method for interpreting dissolved gas analysis test data.

Delta-X Research offers market-leading software for managing and interpreting test and monitor data for high-voltage equipment such as the large power transformers found in electrical substations. In particular, the company provides expert analysis of insulating fluid test data, including dissolved gas analysis (DGA), that can indicate if the equipment is experiencing a fault or requires other maintenance.

By automatically processing both online monitor data and offline test data using advanced analytics, the company provides accurate and timely insights into the health of the utility’s critical assets, so utility staff can confidently optimise maintenance and allocate capital budgets to increase reliability, improve safety, and maximise return on investment.

This new method for interpreting dissolved gas analysis data helps high-voltage equipment experts identify and assess power transformer abnormalities by correlating fault gas production with transformer failures.

Unlike conventional dissolved gas analysis, which uses a simple limits-based approach, Reliability-based DGA applies a statistical model built from real-world transformer reliability data, including failure events.

Reliability-based DGA has been added to Delta-X Research’s flagship software product, Transformer Oil Analyst (TOA4), which is used by more than half of the largest utilities in the US and Canada.

All TOA4 subscribers can now conduct valuable comparisons between conventional DGA and Reliability-based DGA on their own transformer fleets.

Delta-X Research president and CEO John Brett reported: “Early results from this type of comparison show remarkable results.

“One of our customers, a major US utility, asked us to run a comparison of their entire 7,000+ transformer fleet with Reliability-based DGA versus conventional DGA. In this case, Reliability-based DGA uncovered nearly 1,000 transformers showing abnormalities that were not revealed by conventional DGA, while at the same time reducing the number of false alarms substantially. Their subsequent investigations verified that Reliability-based DGA is correct.

“The utility has incorporated Reliability-based DGA as a regular part of their transformer health surveillance process. We anticipate seeing this outcome from many of our customers as they employ Reliability-based DGA.”

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