Combining measurements with models for superior information in hydropower plants

In recent years, key external drivers and technology trends including societal concern for climate change and emphasis on improvement of air quality, rapid expansion of renewable energy sources and emergence of vehicle connectivity have greatly accelerated the move towards electrification for on road vehicle propulsion. In response, Ford has greatly increased its investments in the electrification of its product portfolio. Core to Ford's product portfolio is a set of electrified powertrain system architectures encompassing HEV, PHEV and BEV systems spanning a range of vehicle applications with both lateral (front wheel drive) and longitudinal (rear wheel drive) driveline configurations.

Section: Abstract: Observers State Estimation Extended Kalman Filter ...mentioning

confidence: 93%

Proceedings of the American Modelica Conference 2020, Boulder, Colorado, USA, March 23-25, 2020

Tiller¹,

Tummescheit²,

Vanfretti³

et al. 2020

“…Brembeck (2019) further develops this work and implements a number of practical refinements on the state-of-charge estimator. Other recent work done by Vytvytskyi and Lie (2019) compares the performance of unscented Kalman filters and ensemble Kalman filters for state estimation in hydropower plants and finds that these methods work well in this application. The present paper is an extension of the work presented by Bortoff and Laughman (2019), in which we demonstrated the use of a model-exchange functional mockup unit (FMU) to construct an extended Luenberger observer (ELO) with output injection to estimate the heat capacity of a water-cooled fan coil unit in a building.…”

Section: Introductionmentioning

confidence: 93%

Nonlinear State Estimation with FMI: Tutorial and Applications

Laughman¹,

Bortoff²

2020

One of the key uses enabled by the functional mockup interface (FMI) standard is the ability to combine Modelica models governed by differential-algebraic equations with measurement data to systematically estimate unmeasured quantities in physical systems. While it is clear how this might be done in theory, many implementation challenges can make this difficult in practice. This paper provides a tutorial connecting the mathematical formulation of two different estimators, the extended Kalman filter (EKF) and the ensemble Kalman filter (EnKF), to an FMIbased Modelica implementation of these estimators. The efficacy of these methods are demonstrated on an example of a small motor model and a larger thermodynamic model of a building, and some of the advantages and disadvantages of this FMI-based approach to estimation are discussed, as well as limitations of FMI associated with constraint management for these estimation methods. The code for the motor example is publicly available and is attached to this publication.

“…The library also has draft models for a Pelton turbine, Francis turbine friction model, surge shaft, open channel flow, and a hydrology model. In addition, some accompanying work on analysis tools has been developed in scripting languages (Python, Julia) related to state estimation, structural analysis, etc (Vytvytskyi and Lie, 2019b). The library has been tested on real power plant data (Vytvytskyi and Lie, 2019a).…”

Section: Previous Studiesmentioning

confidence: 99%

Mechanistic modeling of different types of surge tanks and draft tubes for hydropower plants

Pandey¹,

Lie²

2021

Self Cite

OpenHPL is an open-source hydropower library consisting of models for hydropower components that are developed based on mass and 1D momentum balance. It consists of mechanistic models for the flow of water in filled pipes (inelastic and elastic walls, incompressible and compressible water), a mechanistic model of a Francis turbine (including design of turbine parameters), friction models, etc. This paper includes an extension of OpenHPL with mechanistic models of different types of surge tanks (sharp orifice type, throttle valve surge tank, air-cushion surge tank) and draft tubes (conical diffusers and Moody spreading pipes). The simulated response of the models is presented using a case study of real hydro power plants.