EVALUATING AND OPTIMIZING UTILITY PLANT OPERATION USING TREND DATA

Abstract

Trend data have been the main source for utility plant evaluation and optimization. However, the current practice of trend data processing has not been well addressed in previous research as an important part of the workflow. As a consequence, the evaluation and optimization process can fail due to unreliable data, as the performance indicators are improperly estimated. The chilled water systems in the Texas A&M University utility plant have been investigated in this thesis. The hourly average timeseries data of chilled water systems are categorized with various methods in order to validate the reliability of meter records and performance benchmarking. After-processing, data are input for characteristic performance mappings and anomaly detection, which will help the plant operator in fault diagnosis and improving the performance of the chilled water systems. The outputs of this data-only–based validation process have been aligned with an on-site commissioning report, which requires an investment of labor and resources. It can be applied in other utility plants with similar configuration.