Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: Sequential, multi-metric tuning of the controllers

Abstract

Increasing renewable energy usage puts extra pressure on decision-making in river hydropower systems. Decision support tools are used for near-future forecasting of the water available. Model-driven forecasting used for river state estimation often provides bad results due to numerous uncertainties. False inflows and poor initialization are some of the uncertainty sources. To overcome this, standard data assimilation (DA) techniques (e.g., ensemble Kalman filter) are used, which are not always applicable in real systems. This paper presents further insight into the novel, tailor-made model update algorithm based on control theory. According to water-level measurements over the system, the model is controlled and continuously updated using proportional-integrative-derivative (PID) controller(s). Implementation of the PID controllers requires the controllers' parameters estimation (tuning). This research deals with this task by presenting sequential, multi-metric procedure, applicable for controllers' initial tuning. The proposed tuning method is tested on the Iron Gate hydropower system in Serbia, showing satisfying results.