Optimizing Online Feedback for Centrifugal Compressor Setpoint Tracking

核心概念

The author explores the tuning of Online Feedback Optimization controllers to improve tracking performance in centrifugal compressors.

摘要

The content delves into the optimization of Online Feedback Optimization (OFO) controllers for setpoint tracking in centrifugal compressors. It discusses the challenges of setting OFO parameters and sampling time, proposing a tuning method to enhance tracking performance. The study quantifies the impact of OFO parameters on tracking error and oscillations, presenting a tuning framework validated in a pressure controller scenario. Results show significant improvement in tracking performance compared to manual tuning based on steady state.

統計資料

OFO controllers yield up to 87% better tracking performance than manual tuning.
Tuning approach improves response shaping by adjusting sampling time.
Impact analysis shows a trade-off between error reduction and oscillatory behavior.

引述

"Successful applications of OFO include electric grids and compressor stations."
"Simultaneous tuning of sampling time and OFO parameters yields better tracking performance."
"Tuning framework enhances response properties with respect to error tracking and oscillatory behavior."

從以下內容提煉的關鍵洞見

Tuning of Online Feedback Optimization for setpoint tracking in centrifugal compressors

by Marta Zagoro... 於 arxiv.org 03-12-2024

https://arxiv.org/pdf/2312.01996.pdf

深入探究

How can surrogate optimization methods be utilized for more efficient tuning processes

Surrogate optimization methods can be leveraged to enhance the efficiency of tuning processes in various ways. One key benefit is the ability to approximate complex and computationally expensive functions with simpler surrogate models, such as Gaussian Processes or neural networks. By using these surrogate models, the optimization algorithm can explore the parameter space more efficiently, reducing the number of actual evaluations needed. This leads to faster convergence and reduced computational cost during the tuning process.
Additionally, surrogate optimization methods enable parallel evaluations of different parameter configurations by utilizing multiple instances of the surrogate model simultaneously. This parallelization accelerates the search for optimal parameters and allows for a more thorough exploration of the parameter space within a shorter timeframe.
Moreover, surrogates can aid in handling noisy objective functions or constraints by smoothing out irregularities and providing a more stable landscape for optimization algorithms to navigate. This robustness ensures that small fluctuations or uncertainties do not significantly impact the tuning process's effectiveness.
In summary, integrating surrogate optimization methods into tuning processes offers advantages such as accelerated convergence, reduced computational burden, enhanced parallelization capabilities, and improved resilience against noise in objective functions or constraints.

What are the implications of neglecting timescale separation in OFO controllers

Neglecting timescale separation in Online Feedback Optimization (OFO) controllers can have significant implications on controller performance and system stability. Timescale separation refers to designing controllers where certain components operate at slower timescales than others within a dynamic system. In OFO controllers specifically designed for setpoint tracking applications like those discussed in centrifugal compressors control systems context provided above), neglecting timescale separation may lead to several challenges:

Performance Degradation: Without proper timescale separation between controller dynamics and system dynamics (e.g., compressor behavior), it becomes challenging to achieve accurate setpoint tracking due to mismatched response rates.

Instability: Neglecting timescale separation could result in unstable control behaviors where rapid adjustments from OFO algorithms might introduce oscillations or overshoots that destabilize system operation.

Convergence Issues: The lack of distinct timescales may hinder convergence properties of OFO algorithms since fast-changing dynamics might interfere with iterative optimizations aimed at reaching optimal operating points.

Suboptimal Control Actions: Controllers without appropriate timescale separations may exhibit suboptimal control actions due to delayed responses or overly aggressive adjustments that do not align well with system requirements.

Therefore, ensuring proper timescale separation is crucial when designing OFO controllers for dynamic systems like centrifugal compressors as it directly impacts performance quality, stability characteristics, convergence speed, and overall control effectiveness.

How can machine learning algorithms be integrated into the optimization process for improved results

Integrating machine learning algorithms into the optimization process alongside traditional techniques like gradient-based approaches can offer several benefits for improving results:

Enhanced Exploration: Machine learning algorithms excel at exploring complex high-dimensional spaces efficiently compared to traditional methods alone which rely on gradients or heuristics.

Model Adaptation: ML models can adapt dynamically based on feedback data during runtime optimizing their predictions continually leading towards better decision-making over time.

3..Handling Nonlinearities: Machine learning techniques are adept at capturing nonlinear relationships present in many real-world problems allowing them effectively model intricate interactions between variables that conventional methods struggle with
4..Improved Robustness: ML algorithms provide robustness against noisy data by generalizing patterns from training data enabling them handle uncertainty better than deterministic approaches
By combining machine learning capabilities with existing optimization frameworks like Online Feedback Optimization (OFO), practitioners gain access advanced tools capable addressing modern-day challenges across various domains including but not limited power grids management , industrial automation etc .