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This paper presents the physical operating principles of several micro wind turbines based on different aerodynamic forces: drag-type Vertical Axis Wind Turbine (VAWT) and lift-type Horizontal Axis Wind Turbine (HAWT). All these devices share the similarity of exploiting the same mechanical-to-electrical conversion: the electrostatic conversion. This type of conversion is based on capacitance variations induced by the motion between a rotor and a stator and requires a source of polarization. We will focus our study on two technologies to polarize the capacitive structure: the use of electrets and the exploitation of triboelectricity. Some experiments conducted in a low-speed wind tunnel between 0 and 20m.s-1 have highlighted power flux densities from 0 to 150{mu}W.cm-2 corresponding to power coefficients of 0 and 9% respectively. Among these results, we can especially retain an ultralow speed operation, which has never been reached until now, in terms of speed and efficiency (9% of efficiency at 1m.s-1). Finally, we will end up comparing different types of circuits to supply a temperature/acceleration sensor, in order to complete the energy harvesting chain.
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Background. This paper study statistical data gathered from wind turbines located on the territory of the Republic of Poland. The research is aimed to construct the stochastic model that predicts the change of wind speed with time. Purpose. The purpose of this work is to find the optimal distribution for the approximation of available statistical data on wind speed. Methods. We consider four distributions of a random variable: Log-Normal, Weibull, Gamma and Beta. In order to evaluate the parameters of distributions we use method of maximum likelihood. To assess the the results of approximation we use a quantile-quantile plot. Results. All the considered distributions properly approximate the available data. The Weibull distribution shows the best results for the extreme values of the wind speed. Conclusions. The results of the analysis are consistent with the common practice of using the Weibull distribution for wind speed modeling. In the future we plan to compare the results obtained with a much larger data set as well as to build a stochastic model of the evolution of the wind speed depending on time.
Condition based maintenance is a modern approach to maintenance which has been successfully used in several industrial sectors. In this paper we present a concrete statistical approach to condition based maintenance for wind turbine by applying ideas from statistical process control. A specific problem in wind turbine maintenance is that failures of a certain part may have causes that originate in other parts a long time ago. This calls for methods that can produce timely warnings by combining sensor data from different sources. Our method improves on existing methods used in wind turbine maintenance by using adaptive alarm thresholds for the monitored parameters that correct for values of other relevant parameters. We illustrate our method with a case study that shows that our method is able to predict upcoming failures much earlier than currently used methods.
Modern radio astronomical facilities are able to detect extremely weak electromagnetic signals not only from the universe but also from man-made radio frequency interference of various origins. These range from wanted signals to unwanted out-of-band emission of radio services and applications to electromagnetic interference produced by all kinds of electronic and electric devices. Energy harvesting wind turbines are not only equipped with electric power conversion hardware but also copious amounts of electronics to control and monitor the turbines. A wind turbine in the vicinity of a radio telescope could therefore lead to harmful interference, corrupting the measured astronomical data. Many observatories seek to coordinate placement of new wind farms with wind turbine manufacturers and operators, as well as with the local planning authorities, to avoid such a situation. In our study, we provide examples as well as guidelines for the determination of the separation distances between wind turbines and radio observatories, to enable a benign co-existence for both. The proposed calculations entail three basic steps. At first, the anticipated maximum emitted power level based on the European EN 550011 (CISPR-11) standard, which applies to industrial devices, is determined. Then secondly, the propagation loss along the path to the radio receiver is computed via a model provided by the international telecommunication union. Finally, the received power is compared to the permitted power limit that pertains in the protected radio astronomical observing band under consideration. This procedure may be carried out for each location around a telescope site, in order to obtain a map of potentially problematic wind turbine positions.
The estimation of extreme loads from waves is an essential part of the design of an offshore wind turbine. Standard design codes suggest to either use simplified methods based on regular waves, or to perform fully nonlinear computations. The former might not provide an accurate representation of the extreme waves, while the latter is computationally too intensive for design iterations. We address these limitations by using the fully nonlinear solver OceanWave3D to establish the DeRisk database, a large dataset of extreme waves kinematics in a two-dimensional domain. From the database, which is open and freely available, a designer can extract fully-nonlinear wave kinematics for a wave condition and water depth of interest by identifying a suitable computation in the database and, if needed, by Froude-scaling the kinematics. The nonlinear solver is validated against the DeRisk model experiments at two different water depths, $33.0 [m]$ and $20.0 [m]$, and an excellent agreement is found for the analyzed cases. The experiments are used to calibrate OceanWave3Ds numerical breaking filter constant, and the best agreement is found for $beta=0.5$. We compare the experimental static force with predictions by the DeRisk database and the Rainey force model, and with state-of-the-art industrial practices. For milder storms, we find a good agreement in the predicted extreme force between the present methodology and the standard methodologies. At the deep location and for stronger storms, the largest loads are given by slamming loads due to breaking waves. In this condition, the database methodology is less accurate than the embedded stream function method and more accurate than the WiFi JIP methodology, providing generally nonconservative estimates. For strong storms at the shallower location, where wave breaking is less dominating, the database methodology is the most accurate overall.
We suggest a mathematical model for computing and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterium takes into account the current ages of the key components, the major maintenance costs including eventual energy production losses as well as the available data monitoring the condition of the wind turbines. We illustrate our approach with a case study based on data collected from several wind farms located in Sweden. Our results show that preventive maintenance planning gives some effect, if the wind turbine components in question live significantly shorter than the turbine itself.
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