Uncertainty-based perturb and observe for data-driven optimization
For industrial applications where continuous perturbation is undesirable, this method offers a more efficient alternative by perturbing only when needed.
The paper introduces an uncertainty-based perturb-and-observe method that reduces the number of perturbations needed for data-driven optimization of time-varying processes, outperforming standard P&O and three other methods in a photovoltaic solar array simulation.
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.