The objective of this study is to develop a model-free optimization algorithm to improve the total wind farm power production in a cooperative game framework. Conventionally, for a given wind condition, an individual wind turbine maximizes its own power production without taking into consideration the conditions of other wind turbines. Under this greedy control strategy, the wake formed by the upstream wind turbine, due to the reduced wind speed and the increased turbulence intensity inside the wake, would affect and lower the power productions of the downstream wind turbines. To increase the overall wind farm power production, researchers have proposed cooperative wind turbine control approaches to coordinate the actions that mitigate the wake interference among the wind turbines and thus increase the total wind farm power production. This study explores the use of a data-driven optimization approach to identify the optimum coordinated control actions in real time using limited amount of data. Specifically, we propose the Bayesian Ascent (BA) method that combines the strengths of Bayesian optimization and trust region optimization algorithms. Using Gaussian Process regression, BA requires only a few number of data points to model the complex target system. Furthermore, due to the use of trust region constraint on sampling procedure, BA tends to increase the target value and converge toward near the optimum. Simulation studies using analytical functions show that the BA method can achieve an almost monotone increase in a target value with rapid convergence. BA is also implemented and tested in a laboratory setting to maximize the total power using two scaled wind turbine models.
A vibration-based energy harvester is built upon the idea of transforming mechanical vibration of an inertial frame into electrical power. When the excitation frequency matches the natural frequency of the harvester, the energy generated by mechanical vibration is maximized. However, the reliance on resonance inevitably poses a robustness issue, in that power production drops significantly when the excitation frequency is slightly off from the tuned natural frequency of the harvester. To reduce the sensitivity of power output on the resonance, this paper proposes a novel concept of vibration based energy harvester in which both the magnets and the coils are attached to the vibrating cantilevers whose natural frequencies are separated with an optimally chosen frequency band. Due to the relative motions between the coil and the magnet cantilevers, the proposed energy harvester generates higher power over a wider range of excitation frequency compared to a conventional inertial frame based energy harvester. The improvements in the power output and the robustness are validated by experiments in the laboratory and on a bridge.
The objective of this study is to improve the cost-effectiveness and production efficiency of wind farms using cooperative control. The key factors in determining the power production and the loading for a wind turbine are the nacelle yaw and blade pitch angles. However, the nacelle and blade angles may adjust the wake direction and intensity in a way that may adversely affect the performance of other wind turbines in the wind farm. Conventional wind-turbine control methods maximize the power production of a single turbine, but can lower the overall wind-farm power efficiency due to wake interference. This paper introduces a cooperative game concept to derive the power production of individual wind turbine so that the total wind-farm power efficiency is optimized. Based on a wake interaction model relating the yaw offset angles and the induction factors of wind turbines to the wind speeds experienced by the wind turbines, an optimization problem is formulated with the objective of maximizing the sum of the power production of a wind farm. A steepest descent algorithm is applied to find the optimal combination of yaw offset angles and the induction factors that increases the total wind farm power production. Numerical simulations show that the cooperative control strategy can increase the power productions in a wind farm.