PSE&G has been collecting substation transformer health data for the past 13 years to help predict when an asset should be replaced. The replacement algorithm is based on criticality of asset, asset age, asset health condition and corrective maintenance history. For the past several years PSE&G has been collecting more real time data on transformers and storing the data in the long-term archives. This data is being processed specifically so that the transformer Loss of Life can be calculated. This paper discusses how “Big Data” improves analytics (using PI real-time and historical archive data with SAP HANA advanced analytic tools) and helps to identify true age of a transformer, overloaded transformers as well as predicting future condition of a transformer.
Speaker
Angela Rothweiler
Angela is a Principal Engineer at Public Service Electric and Gas (PSE&G), in Electric Asset Strategy
Department. She has been with the company for 12 years and focuses on data integration and condition based
maintenance programs. She has a master in software engineering.