Uncertainty-based perturb and observe for data-driven optimization

TL;DR

This paper introduces an uncertainty-based perturb-and-observe optimization method that minimizes perturbations and effectively tracks time-varying optima, suitable for real-world applications.

Contribution

It develops a novel perturb-and-observe approach that reduces perturbations and maintains tracking of optima in uncertain, dynamic environments.

Findings

Outperforms standard perturb and observe methods in simulations.

Reduces the number of perturbations needed for convergence.

Converges to the optimum under mild conditions.

Abstract

Data-based adaptive optimization methods hold great promise for the performance optimization of uncertain, time-varying processes. However, current methods are often based on continuous perturbation which is in general undesired for real-life (e.g., industrial) applications. In this paper, a new uncertainty-based perturb-and-observe method is developed that addresses this limitation and reduces the required number of perturbations, while retaining the capability to track time-varying optima. The method is based on the philosophy of `only perturbing when needed,' and is shown to converge to the optimum under mild conditions. A simulation-based case study on a photo-voltaic solar array demonstrates that it can outperform the standard perturb and observe approach as well as three other data-based optimization methods.