Search arXivSearch

arXiv · 2305.11710

Quasi-static closed-loop wind-farm control for combined power and fatigue optimization

Abstract

We develop a methodology for combined power and loads optimization by coupling a surrogate loads model with an analytical quasi-static Gaussian wake merging model. The look-up table based fatigue model is developed offline through a series of OpenFast simulations, covering different operational states of a DTU 10 MW reference wind turbine, and verified against large eddy simulations with aeroelastic coupling. Subsequently, optimal control set points for the TotalControl reference wind power plant are obtained using the analytical model, and tested in a wind farm emulator that is based on large eddy simulations. The wake model is calibrated online using in a quasi-static closed-loop manner. Benefits of the closed-loop controller are exhibited via comparison of farm performance against greedy operation and against open-loop control results obtained without feedback or calibration. Results show that the closed-loop control out performs open-loop control, with farm configurations with deep turbine arrays showcasing the highest gains. Inclusion of fatigue in the cost function through the developed LUT also leads to interesting insights, with reduced blade root fatigue loading without significant decrease in power production when compared to open-loop control. A case study is also performed which showcases real world applications for the developed closed-loop controller and the load LUT, in which the closed-loop controller is shown to react to scenarios with turbine operation shut-down, optimizing the yaw angles online to maximize performance for the new layout.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ishaan Sood, Christophe del Fosse et d'Espierres, Johan Meyers. 2023-05-19. Quasi-static closed-loop wind-farm control for combined power and fatigue optimization. https://arxiv.org/abs/2305.11710

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Failure-Aware Iterative Learning of State-Control Invariant Sets

In this paper, we address the problem of computing maximal state-control invariant sets for deterministic linear systems using failing trajectories. We introduce the concept of state-control invariance, which extends control invariance from the state space to the joint state-control space. The maximal state-control invariant (MSCI) set simultaneously encodes the maximal control invariant set (MCI) and, for each state in the MCI, the set of control inputs that preserve invariance. We prove that the state projection of the MSCI is the MCI and the state-dependent sections of the MSCI are the admissible invariance-preserving inputs. Building on this framework, we develop a Failure-Aware Iterative Learning (FAIL) algorithm for deterministic linear time-invariant systems with polytopic constraints. The algorithm iteratively updates a constraint set in the state-control space by learning predecessor halfspaces from one-step failing state-input pairs, without knowing the dynamics. For each failure, FAIL learns the violated halfspaces of the predecessor of the constraint set by a regression on failing trajectories. We prove that the learned constraint set converges monotonically to the MSCI. Numerical experiments on a double integrator system validate the proposed approach.

eess.SY

Consensus and Synchronization of Multi-agent Systems over Finite Fields - Graph Topologies

This paper presents cooperative protocols for multi-agent systems with agents having a finite state-space. Both scalar single-integrator consensus and general LTI system synchronization are considered. Systems having a finite state-space describe agents with minimal memory capacity processing only a finite alphabet. Such systems are remarkably resilient to communication noise. The crucial problem, however, is to construct the admissible communication topology, which is NP-hard. We address this by efficiently exploring the subsets of admissible graph matrices and propose two new algorithms to generate them. Simulations validate the proposed approach.

eess.SY

Extracting Exact Lie Derivatives Without Backpropagation: A Dual Compiler for Neural Control Barrier Functions

A safety filter based on a neural control barrier function (CBF) deployed in an embedded control loop evaluates, at each control cycle, the trained network and its Lie derivatives along the system vector fields, under the memory and worst-case execution time (WCET) constraints that safety-oriented coding standards impose. Reverse-mode automatic differentiation, by which training frameworks obtain these derivatives, retains an activation cache whose size grows with the sum of the layer widths, and general-purpose differentiation runtimes allocate the computational graph from the heap at each call. This paper presents a compiler that evaluates a neural CBF and its exact Lie derivatives by forward-mode dual-number arithmetic. The compiler emits self-contained C++ code in which a single forward pass, without backpropagation, returns the barrier value and its exact Lie derivative along a given vector field; the drift and input Lie derivatives of the safety constraint are obtained from one such pass per vector field, and a second-order extension based on hyper-dual numbers returns the exact second-order Lie derivatives required by CBFs of relative degree two. The dual forward pass requires a workspace bounded by four times the widest layer, independent of network depth, and the emitted code contains no allocation call sites, so the absence of dynamic allocation is verifiable by inspection of the code. On an ESP32-S3 microcontroller, the compiled filter assembles the complete safety constraint in under one millisecond from statically allocated buffers of at most 768 bytes, and the maximum execution time over 1000 evaluations lies within 5% of the median in all three examples, whereas a heap-allocating reverse-mode baseline shows maxima 33% and 70% above its median in the two first-order examples. The compiler and the embedded experiments are released as open-source software.

eess.SY