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Data Management System for Dam Monitoring of Hydropower Projects

Author(s): Stefano Vincenzo De Simone; Michael Natschke

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Keywords: Dam Monitoring; Infrastructure analytics; Hydrostatic seasonal forecast

Abstract: The assessment of dam safety and risk relies on a sophisticated monitoring and assessment of various parameters such as movement, pressure, temperature, water level, percolation etc. Our focus is the integration of such data in a state-of-the-art framework for data acquisition and storage, primary and secondary data validation procedures as well as alarming. Main objective of its implementation is to adapt the dam operator’s business processes and to enable a clear, efficient and safe execution of the monitoring activities. The primary validation layer aims at the individual validation of scalar time series by checking the data range, rate-of-change, persistent readings, among others. In a second step, the inner consistency of the data is addressed by the application of the well-established Hydrostatic-Season-Time (HST) Model. In this model-based validation, we fit the model into a moving window of historical data by a parameter identification. The deviation between simulated and observed parameters enables the detection of anomalies of the dam behaviour, which are the basis for the downstream alarming. We present the application of the framework to a reservoir system of 12 dams of various types of Enerjisa, a joint venture of Sabanci and E. On. The company distributes and supplies electricity serving 9 million accounts and with about 2.6 GW of installed generating capacity, of which50% are renewables. The new dam monitoring solution is designed as modern information environment for office and field. It supplies all required information in dashboard style screens and easy to use field applications with fully automated background processes for data import and validation. This led to an optimization of Enerjisa’s business processes saving time and providing up-to-date information for the decision support.


Year: 2018

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