Model-Guided Learning for Wind Farm Power Optimization

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Accurately modeling the interactions is challenging due to the highly complex nature of the wakes and this limits the performance of model-based wind farm power optimization methods. There are also data-driven approaches, which do not require a system model. However, they generally require a large number of measurement data and the convergence speed can be slow. To address these limitations, this article proposes a model-guided learning (MGL) method for wind farm to improve its power output by leveraging the knowledge of the available simplified power generation model and learning from the real-time power generation data. The proposed method can quickly increase the power output of the wind farm, guarantee implemented control actions to satisfy the control constraints of all turbines, and have the ability to find the optimal solution of the power optimization problem. The presented method is then extended to deal with time-varying wind conditions using a hierarchical framework. Simulation results indicate that the proposed scheme can efficiently improve the power output of the wind farm in different wind conditions compared with some benchmarks. It shows a power efficiency gain of 2.5% over greedy policy and 1.2% than the model-based gradient method in given complex wind conditions, which are substantial improvements in the performance for the considered wind farm power optimization problem.

I. Introduction

Renewable energy plays a vital role in mitigating the climate change, environmental pollution, and increasing electricity demands. Wind energy is one of the most environmental friendly and cost-competitive renewable energy sources, and its utilization is fast-growing in an unprecedented rapid pace [1]. In 2019, there was 60.4 GW global new wind power installation—an increase of 19% compared with that of 2018. The total capacity has risen up to 651 GW with a growth of 10% compared with 2018 [2]. Wind power has met 15% of the EU’s power demand on average and would meet more than 50% by 2050 as currently planned [3]. With this promising trend, advanced wind farm control strategies are becoming increasingly important to efficiently use the wind energy, increase power extraction, and develop more profitable wind farms [4], [5].