A Machine Learning Method for Modeling Wind Farm Fatigue Load

Wake steering control can significantly improve the overall power production of wind farms. However, it also increases fatigue damage on downstream wind turbines. Therefore, optimizing fatigue loads in wake steering control has become a hot research topic. Accurately predicting farm fatigue loads has always been challenging. The current interpolation method for farm-level fatigue loads estimation is also known as the look-up table (LUT) method. However, the LUT method is less accurate because it is challenging to map the highly nonlinear characteristics of fatigue load. This paper proposes a machine-learning algorithm based on the Gaussian process (GP) to predict the farm-level fatigue load under yaw misalignment. Firstly, a series of simulations with yaw misalignment were designed to obtain the original load data, which considered the wake interaction between turbines. Secondly, the rainflow counting and Palmgren miner rules were introduced to transfer the original load to damage equivalent load. Finally, the GP model trained by inputs and outputs predicts the fatigue load. GP has more accurate predictions because it is suitable for mapping the nonlinear between fatigue load and yaw misalignment. The case study shows that compared to LUT, the accuracy of GP improves by 17% (RMSE) and 0.6% (MAE) at the blade root edgewise moment and 51.87% (RMSE) and 1.78% (MAE) at the blade root flapwise moment

Optimization of Micro Multi-Carrier Energy Hub Operation Under Uncertain Predictions

Finding an optimal schedule(s) for the buildings’energy equipment cluster is vital to realize sustainabledevelopment and energy-saving goals. However, high-impactuncertainties pose critical challenges in this regard. To relaxthese challenges, this paper develops an optimization model foroperating the buildings’ energy equipment cluster underuncertain predictions whose principal elements are the energyhub (EH) concept and the two-point estimate (TPE) method.The EH concept is used to find levels of the degree of freedomfor optimization by modeling efficiently how multi-carrierenergy resources and demands can be connected through thiscluster of converters, conditioners, storage, and others. The TPEmethod is, however, used to improve the reliability androbustness of the model’s predictions, leading to better decisionmakingunder uncertainty. The TPE method integrates highimpactuncertainties related to multi-carrier energy prices anddemands and the production capacity of renewable energyresources in optimization. The proposed optimization model hasbeen applied to an industrial building, and its sufficiency andprofitableness are examined in different scenarios

Discrete-Time Domain Modeling of a High-Power Medium-Voltage Resonant Converter

LLC resonant converters are widely used indiverse industrial applications, due in part to their highefficiency and high power density. In spite of their numerousadvantages, these converters are still considered themost challenging converters from a modeling and controlperspective. Several factors contribute to this complexity,including the nonlinear behavior and different operatingmodes. Therefore, a high-power medium-voltage resonantconverter is modeled in this manuscript, and its dynamicbehavior is investigated. The nonlinear model simulationperformed by MATLAB/Simulink and the electrical circuitsimulation performed by PLECS are then compared toverify the accuracy of the obtained model