Combining measurements with models for superior information in hydropower plants

• Published in: Flow measurement and instrumentation 2019-10, Vol.69, p.101582, Article 101582
• Main Authors: Vytvytskyi, Liubomyr, Lie, Bernt
• Format: Article
• Language: English
• Subjects: Hydropower; Modelling; Nonlinear Kalman filters; OpenModelica; State estimation
• ISSN: 0955-5986; 1873-6998
• DOI: 10.1016/j.flowmeasinst.2019.101582

Water flow and pressure measurements play essential roles in the operation of hydropower plants. For all methods of measuring flow and pressure, there is a level of uncertainty with regards to sensor noise and sensor failure. In addition, measurements in key locations are hard to obtain. A combination of measurements with a mathematical model of a hydropower plant can be used to improve information about and operation of the hydropower system. This paper describes the possibility of using nonlinear estimators such as Ensemble or Unscented Kalman filters in order to estimate the states of the hydropower system based on water flow and/or pressure measurements. The implementation of the estimators is done in Python using a Python API for operating OpenModelica simulations, where the hydropower system is modeled using an in-house hydropower Modelica library — OpenHPL. •Improved information about and operation of hydropower systems by measurements assimilation with a mathematical plant model.•Investigation of the possibility of state estimation for a hydropower system using Unscented and Ensemble Kalman filters.•Implementation of the state estimators (UKF and EnKF) in Python using a hydropower model in OpenModelica via Python API.