Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control

This paper introduces a novel data driven yaw control algorithm synthesis method based on Reinforcement Learning (RL) for a variable pitch variable speed wind turbine. Yaw control has not been extendedly studied in the literature; in fact, most of the currently considered developments in the scope of the wind energy are oriented to the pitch and speed control. The most important drawbacks of the yaw control are the very large time constants and the strict yaw angle change rate constraints due to the high mechanical loads when the wind turbine angle is changed in order to adequate it to the wind speed orientation. An optimal yaw control algorithm needs to be designed in order to adapt the rotor orientation depending on the wind turbine dynamics and the local wind speed regime. Consequently, the biggest challenge of the yaw control algorithm is to decide the moment and the quantity of the wind turbine orientation variation to achieve the highest quantity of power at each instant, taking into account the constraints derived from the mechanical limitations of the yawing system and the mechanical loads. In this paper, a novel based algorithm based on the RL Q-Learning algorithm is introduced. The first step is to obtain a model of the power generated by the wind turbine (a real onshore wind turbine in this paper) through a power curve, that in conjunction with a conventional proportional regulator will be used to obtain a dataset that explains the actual behaviour of the real wind turbine when a variety of different yaw control commands are imposed. That knowledge is then used to learn the best control action for each different state of the wind turbine with respect to the wind direction represented by the yaw angle, storing that knowledge in a matrix Q(s,a). The last step is to model that matrix through a MultiLayer Perceptron with BackPropagation (MLP-BP) Artificial Neural Network (ANN) to avoid large matrix management and quantification problems. Once that the optimal yaw controller has been synthetized, its performance has been assessed using a number of wind speed realizations obtained using the software application TurbSim, in order to analyze how the introduced novel algorithm deals with different wind speed scenarios

Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

ABSTRACT: Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m2) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass.The work reported in this paper was partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and project KK-2018/00071 of the Elkartek 2018 funding program of the Basque Government

Longitudinal wind speed time series generation to wind turbine controllers tuning

Although there are a wide variety of applications that require wind speed time series (WSTS), this paper emphases on WSTS to be used into wind turbine controllers tuning. These simulations involve several WSTS to perform a proper assessment. These WSTS must assure realistic wind speed variations such as wind gusts and include some rare events such as extreme wind situations. The architecture proposed to generate this WSTS is based on autoregressive models with certain post-processing. The methodology used is entirely described by precise notation as well as it is parametrized by means of data gathered from a weather station. Two main different simulations are performed and assessment; the first simulation is fed by weather data with high wind speed and great variability. The second simulation, on the opposite, use calm wind speed as a data source

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Object detection is an essential and impactful technology in various fields due to its ability to automatically locate and identify objects in images or videos. In addition, object-distance estimation is a fundamental problem in 3D vision and scene perception. In this paper, we propose a simultaneous object-detection and distance-estimation algorithm based on YOLOv5 for obstacle detection in indoor autonomous vehicles. This method estimates the distances to the desired obstacles using a single monocular camera that does not require calibration. On the one hand, we train the algorithm with the KITTI dataset, which is an autonomous driving vision dataset that provides labels for object detection and distance prediction. On the other hand, we collect and label 100 images from a custom environment. Then, we apply data augmentation and transfer learning to generate a fast, accurate, and cost-effective model for the custom environment. The results show a performance of mAP0.5:0.95 of more than 75% for object detection and 0.71 m of mean absolute error in distance prediction, which are easily scalable with the labeling of a larger amount of data. Finally, we compare our method with other similar state-of-the-art approaches

Design and Implementation of a Maximum Power Point Tracking System for a Piezoelectric Wind Energy Harvester Generating High Harmonicity

In this work, a maximum power point tracking (MPPT) system for its application to a new piezoelectric wind energy harvester (PWEH) has been designed and implemented. The motivation for such MPPT unit comes from the power scales of the piezoelectric layers being in the order of μW. In addition, the output generates highly disturbed voltage waveforms with high total harmonic distortion (THD), thereby high THD values cause a certain power loss at the output of the PWEH system and an intense motivation is given to design and implement the system. The proposed MPPT system is widely used for many different harvesting studies, however, in this paper it has been used at the first time for such a distorted waveform to our best knowledge. The MPPT consists of a rectifier unit storing the rectified energy into a capacitor with a certain voltage called VOC (i.e., the open circuit voltage of the harvester), then a dc-dc converter is used with the help of the MPPT unit using the half of VOC as the critical value for the performance of the control. It has been demonstrated that the power loss is nearly half of the power for the MPPT-free system, the efficiency has been increased with a rate of 98% and power consumption is measured as low as 5.29 μ

Power control optimization of a new contactless piezoelectric harvester

WOS: 000406726000052In this study, we propose an optimization scheme for the control of a piezoelectric wind energy harvester. The harvester is constructed by a blade in front and a magnet in the rear in order to sustain a magnetic repulsion by another magnet located on the stable harvester body in a contactless manner. For such a new harvester, the control scheme is missing in the literature in the sense that the harvester is new and an overall optimization study is required for such a device. In that context, the optimization has been realized by using a new current control law based on the harvester piezoelectric terminal voltage and the layer bending. The proposed control law can impose a second order linear dynamics although the magnetic effects can yield to nonlinear magnetic force relation. In order to improve the new control strategy, a Particle Swarm Optimization algorithm (PSO) has been applied, since there is a nonlinear dependency among the control parameters, the collected energy and the bending force mean values. According to results, the captured electrical power has a high increasing trend with respect to the only-voltage-based (OVB) control as the current study proves. On the contrary, the artifact of the method'is that the obtained power is too low to increase the mean bending forces and it requires much complicated control system. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.European Union Ministry of Turkey [2015-1-TR01-KA203-021342]; National Agency of Turkey [2015-1-TR01-KA203-021342]; Government of the Basque Country; University of the Basque Country UPV/EHU [S-PE11UN112, EHU12/26]The authors are grateful to European Union Ministry of Turkey, National Agency of Turkey for the support of this project under the Project Code: 2015-1-TR01-KA203-021342 entitled Innovative European Studies on Renewable Energy Systems. The fundings from the Government of the Basque Country and the University of the Basque Country UPV/EHU through the SAIOTEK (S-PE11UN112) and EHU12/26 research programs are also acknowledged