Owing to their harsh operating conditions, oil-ﬁlled transformers tend to degrade over time and leak oil, which results in failure or reduced life. The main indicators of transformer health are oil level and top oil temperature. Further, being highly optimized for cost, these transformers have very minimal instrumentation.
Our solution proposes a scheme to virtually sense the oil level using temperature sensors ﬁtted on the transformer housing. A physics-based model of the transformer enables us to relate the temperature measurements to the amount of oil present in it in real-time. This non-invasive solution can be retroﬁtted on any ONAN transformer and can enable real-time condition monitoring of the asset.
The next step in implementation of our solution was ﬁeld deployment. We exploited the features of the MATLAB Production Server™ to deploy our algorithms to the cloud seamlessly. Prototype IoT devices deployed on individual transformers stream data to the cloud where our algorithm generates signals for predictive maintenance of the respective assets.