Nonlinear observer for hydropower system

Abstract

Estimation of unmeasured states plays an essential role in the design of control systems as well as for monitoring of hydropower plants. The standard Kalman filter gives the optimum state estimates for linear systems. However, this optimality is not relevant for nonlinear models and a choice between stochastic and deterministic approaches is not so obvious in this case. Thus the application of a nonlinear observer in a hydropower system is of interest here as an alternative to the widely used extended Kalman filter. This paper provides a study and design of a reduced order nonlinear observer to estimate the states of a hydropower system. Implementation of the nonlinear observer is done in OpenModelica and added to our in-house hydropower Modelica library — OpenHPL, where different models for hydropower systems are assembled. Simulations and analysis of the designed observer are done in Python using a Python API for operating OpenModelica simulations