Joint state-parameter estimation for a control-oriented LES wind farm model

J.W Van Wingerden,L.Y Pao,S Boersma,B.M Doekemeijer

doi:10.1088/1742-6596/1037/3/032013

Abstract

Wind farm control research typically relies on computationally inexpensive, surrogate models for real-time optimization. However, due to the large time delays involved, changing atmospheric conditions and tough-to-model flow and turbine dynamics, these surrogate models need constant calibration. In this paper, a novel real-time (joint state-parameter) estimation solution for a medium-fidelity dynamical wind farm model is presented. In this work, we demonstrate the estimation of the freestream wind speed, local turbulence, and local wind field in a two-turbine wind farm using exclusively turbine power measurements. The estimator employs an Ensemble Kalman filter with a low computational cost of approximately 1.0 s per timestep on a dual-core notebook CPU. This work presents an essential building block for real-time wind farm control using computationally efficient dynamical wind farm models.

Highlights

With the expected growth in our population from 7 billion in 2017 to over 9 billion by 2040, a rapid urbanization that “adds a city the size of Shanghai to the world’s urban population every four months” [1], and a global economy with an average growth rate of 3 − 4% per year [2], the rising demand for energy is not foreseen to slow down any time soon
In this work, we presented an algorithm for real-time joint state-parameter estimation in low- and medium-fidelity dynamical wind farm models
In a high-fidelity simulation, we demonstrated that the local flow field, local turbulence, and freestream wind speed can jointly be estimated for a two-turbine wind farm using exclusively SCADA data, at a low computational cost of 1.0 s per timestep on an Intel i7-6600U dual-core CPU

Summary

Introduction

With the expected growth in our population from 7 billion in 2017 to over 9 billion by 2040, a rapid urbanization that “adds a city the size of Shanghai to the world’s urban population every four months” [1], and a global economy with an average growth rate of 3 − 4% per year [2], the rising demand for energy is not foreseen to slow down any time soon. This paper presents a time-efficient joint state-parameter estimation (“model calibration”) algorithm for the medium-fidelity dynamical model “WindFarmSimulator” (WFSim) [14]. This calibration solution allows wind farm control algorithms relying on WAFpSriilm17,(2e0.1g8., [151])/ 1to deal with time-varying atmospheric conditions and model discrepancies, while maintaining computational efficiency.

Results

Conclusion