記述言語：その他   掲載種別：研究論文（学術雑誌）  

Wind solar towers constitute a fairly new scheme for harvesting renewable energy from solar and wind energy sources. In such a tower, solar radiation is collected and hot air is enforced to go fast through the tower, a process called thermal updraft, which fuels a wind turbine to generate power. Using vortex generators at the top of the tower creates a pressure difference, which increases the thermal updraft. In this work, we describe the setup of a wind solar tower system established at Kyushu University in Japan. Then, we demonstrate how data was collected from this system in order to train regression models for thermal updraft prediction. The feature selection process was guided by sensitivity analysis. After that, several machine learning models were investigated and the most suitable model was selected based on quality and time metrics. The linear regression model was particularly examined in detail, and was shown to have a satisfactory high accuracy of thermal updraft prediction graphically and numerically with a coefficient of determination of R2 = 0.981. We also evaluated a reduced prediction model based on the six most essential features, which could be a reduced model description for the WST. This reduced model showed little performance degradation (R2 = 0.974), with significant reduction in the needed effort and resources, as well as data collection requirements.

DOI： 10.1016/j.renene.2021.06.033

記述言語：その他   掲載種別：研究論文（学術雑誌）  

We utilize the electromagnetically-oriented LTI∅ dimensional basis in the matrix solution of dimensional-analysis (DA) problems involving mainly electromagnetic quantities, whether these quantities are lumped or distributed. Representations in the LTI ∅ basis (compared with the standard MLTI basis) are more informative and much simpler. Moreover, matrix DA computations employing the LTI ∅ basis are more efficient and much less error prone. Extensive discussions of two demonstrative examples expose technical details of a novel DA scheme, and clarify many important facets of modern dimensional analysis.

DOI： 10.33889/IJMEMS.2021.6.2.039

記述言語：その他   掲載種別：研究論文（学術雑誌）  

Kites can be used to harvest wind energy with substantially lower material and environmental footprints and a higher capacity factor than conventional wind turbines. In this paper, we present measurement data from seven individual tow tests with the kite system developed by Kyushu University. This system was designed for 7 kW traction power and comprises an inflatable wing of 6 m2 surface area with a suspended kite control unit that is towed on a relatively short tether of 0.4 m by a truck driving at constant speed along a straight runway. To produce a controlled relative flow environment, the experiment was conducted only when the background wind speed was negligible. We recorded the time-series of 11 different sensor values acquired on the kite, the control unit and the truck. The measured data can be used to assess the effects of the towing speed, the flight mode and the lengths of the control lines on the tether force.

DOI： 10.3390/data5030069

記述言語：英語   掲載種別：研究論文（学術雑誌）  

The traction force of a kite system can be utilized for extracting energy from high-altitude wind. This paper discusses a system identification algorithm derived to obtain real-time governing equations for the kite system based on real-flight data, obtained from a 6 m2 kite power system developed in Kyushu University as an airborne wind energy project. The paper presents the system set-up, the design, the experimental results, a system identification algorithm, and the parameters identified for the kite used. The current stage of the project considers the kite application as a fixed-tether length system with a ground kite control unit. The control strategy is designed to work as a hardware-in the-loop to keep receiving the data and controlling the kite in real time. The experimental tests employed are divided into four distinct ones, and the data of the kite’s attitude, position, and tension forces are recorded. The tension forces resulted from these tests are presented for different wind speeds and flight modes. Ultimately, a novel system identification algorithm that evaluates the correlation between the tension force and the kite’s rolling angle over the four tests is applied, thereby enabling to study the kite behavior as a preliminary step for the achievement of autonomous flight.

DOI： 10.15866/irease.v13i4.18897

記述言語：その他   掲載種別：研究論文（学術雑誌）  

Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.

DOI： 10.3390/en13092367

開催年月日： 2020年1月

記述言語：英語   会議種別：口頭発表（一般）  

国名：アメリカ合衆国  

その他リンク： https://arc.aiaa.org/doi/10.2514/6.2020-1244

開催年月日： 2020年1月

記述言語：英語   会議種別：口頭発表（一般）  

国名：アメリカ合衆国  

その他リンク： https://arc.aiaa.org/doi/10.2514/6.2020-1243

Educational material

Educational material