Rancangan Monitoring Wind Direction Indicator Berbasis Arduino di Bandar Udara Indonesia

Windsock adalah sebagai penanda angin dan relatif kecepatan angin. Alat ini sangat berfungsi di dunia penerbangan. Setiap bandara wajib memiliki windsock sebagai penunjang penentu arah angin. Alat ini dipasang di suatu bandara udara yang dapat dilihat dengan jelas oleh petugas lalu lintas udara (ATC). Tetapi di bandara A.P.T. Pranoto-Samarinda masih menggunakan sistem manual untuk melihat windsock menggunakan teropong jarak jauh. Pengembangan alat ini dilatar belakangi dikarenakan kurang efektif dan efisiennya saat penggunaan windsock yang sudah tersedia di Bandar Udara A.P.T. Pranoto Samarinda. Maka di sini penulis tertarik untuk mengangkat sebuah judul dalam tugas akhir ini yaitu “Rancangan Monitoring Wind Direction Indicator berbasis Arduino di Bandar Udara Aji Pangeran Tumenggung Pranoto Samarinda ”. Hasil akhir yang dicapai dari pengembangan windsock ini adalah dengan tujuan agar tidak mencari arah angin secara manual, dengan dikembangkannya alat ini dapat mencari arah angin secara otomatis. Windsock ini juga dilengkapi dengan wind vane dan wind cone sebagai pengukur arah mata angin dan kecepatan angin disekitar alat tersebut. Perancangan sistem monitoring Wind Direction Indicator berbasis Arduino, diharapkan dapat mendeteksi kecepatan dan arah angin yang mampu memberikan data secara real time. Agar data yang diperoleh dapat tersampaikan kepada masyarakat dengan cepat dan akurat, maka dibutuhkan suatu sistem yang memadai. Sistem yang sekarang ini sedang berkembang pesat yaitu Personal Computer (PC) dan jaringan internet. Perancangan alat Wind Direction Indicator ini terdiri dari hardware yang berupa Arduino, sensor arah dan sensor kecepatan angin. Alat Wind Direction Indicator dapat membantu memudahkan ATC dalam memantau arah dan kecepatan angin yang sangat penting bagi penerbangan, dimana sebelumnya proses ini menggunakan teropong untuk memantau arah angin. Proses pemantauan arah angin menjadi lebih mudah dengan menggunakan alat ini, karena hasil pembacaan arah dan kecepatan angin akan langsung muncul pada monitor.

Moral Education – The Wind Vane for College Teachers to Carry out Curriculum Ideology and Politics

Moral education is the fundamental task of education. It does not only include the inheritance of the fine traditional thoughts of the Chinese nation, but also the deepening of the new era of education concept with the new development in this changing world. Moral education has been widely implemented in the practical education process of colleges and universities and has achieved some outstanding results, but there are still some areas to be improved. Teachers in colleges and universities are the key to implement the concept of moral education, and classroom teaching is the main channel for the implementation of moral education. Therefore, teachers in colleges and universities need to deeply understand the essence and connotation of moral education, accurately grasp the practical problems existing in moral education, focus on the curriculum construction and the main channel of the classroom, and make every effort to exercise curriculum ideology and politics.

Generic and Flexible Unmanned Sailboat for Innovative Education and World Robotic Sailing Championship

Over the past two decades, scholars developed various unmanned sailboat platforms, but most of them have specialized designs and controllers. Whereas these robotic sailboats have good performance with open-source designs, it is actually hard for interested researchers or fans to follow and make their own sailboats with these open-source designs. Thus, in this paper, a generic and flexible unmanned sailboat platform with easy access to the hardware and software architectures is designed and tested. The commonly used 1-m class RC racing sailboat was employed to install Pixhawk V2.4.8, Arduino Mega 2,560, GPS module M8N, custom-designed wind direction sensor, and wireless 433 Mhz telegram. The widely used open-source hardware modules were selected to keep reliable and low-cost hardware setup to emphasize the generality and feasibility of the unmanned sailboat platform. In software architecture, the Pixhawk V2.4.8 provided reliable states’ feedback. The Arduino Mega 2,560 received estimated states from Pixhawk V2.4.8 and the wind vane sensor, and then controlled servo actuators of rudder and sail using simplified algorithms. Due to the complexity of introducing robot operating system and its packages, we designed a generic but real-time software architecture just using Arduino Mega 2,560. A suitable line-of-sight guidance strategy and PID-based controllers were used to let the autonomous sailboat sail at user-defined waypoints. Field tests validated the sailing performance in facing WRSC challenges. Results of fleet race, station keeping, and area scanning proved that our design and algorithms could control the 1-m class RC sailboat with acceptable accuracy. The proposed design and algorithms contributed to developing educational, low-cost, micro class autonomous sailboats with accessible, generic, and flexible hardware and software. Besides, our sailboat platform also facilitates readers to develop similar sailboats with more focus on their missions.

Accuracy of Wind Observations from Open-Ocean Buoys: Correction for Flow Distortion

The comparison of equivalent neutral winds obtained from (i) four WHOI buoys in the subtropics and (ii) scatterometer estimates at those locations reveals a root-mean-square (RMS) difference of 0.56–0.76 m s−1. To investigate this RMS difference, different buoy wind error sources were examined. These buoys are particularly well suited to examine two important sources of buoy wind errors because 1) redundant anemometers and a comparison with numerical flow simulations allow us to quantitatively assess flow distortion errors, and 2) 1-min sampling at the buoys allows us to examine the sensitivity of buoy temporal sampling/averaging in the buoy–scatterometer comparisons. The interanemometer difference varies as a function of wind direction relative to the buoy wind vane and is consistent with the effects of flow distortion expected based on numerical flow simulations. Comparison between the anemometers and scatterometer winds supports the interpretation that the interanemometer disagreement, which can be up to 5% of the wind speed, is due to flow distortion. These insights motivate an empirical correction to the individual anemometer records and subsequent comparison with scatterometer estimates show good agreement.

Live Traps for Adult Brown Marmorated Stink Bugs

Surveillance for detection of the brown marmorated stink bug, Halyomorpha halys, is reliant on sticky panels with aggregation pheromone, which are low cost, but very inefficient (est. 3%). Trapping for adults was conducted in Italy with novel live (or lethal) traps consisting of aggregation pheromone-baited cylinders with a wind vane, with the upwind end covered by mesh and the downwind end sealed by a removable entry-only mesh cone, admitting the attracted bugs. The novel traps caught up to 15-times more adult H. halys than identically-baited sticky panels in two weeks of daily checking (n = 6 replicates) (the new live traps were, in Run 1, 5-, 9-, 15-, 13-, 4-, 12-, 2-fold; and in Run 2, 7-, 1-, 3-, 7-, 6-, 6-, and 5-fold better than sticky traps, daily). The maximum catch of the new traps was 96 live adults in one trap in 24 h and the average improvement was ~7-fold compared with sticky panels. The rotating live traps, which exploit a mesh funnel facing the plume downwind that proved useful for collecting adults, could also be used to kill bugs. We expect that commercially-available traps could replace the crude prototypes we constructed quickly from local materials, at low cost, as long as the principles of a suitable plume structure were observed, as we discuss. The traps could be useful for the sterile insect technique, supporting rearing colonies, or to kill bugs.

Wind direction estimation using SCADA data with consensus-based optimization

Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

A Framework for Autonomous Wind Farms: Wind Direction Consensus

Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquistion (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for implementing an autonomous wind farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more robust estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbine spends yawing due to a more robust input to the yaw controller, and increase resiliency to faulty wind-vane measurements.

Bioinspired wind field estimation—part 1: Angle of attack measurements through surface pressure distribution

One of the major challenges of Mini-Unmanned Aerial Vehicle flight is the unsteady interaction with turbulent environment while flying in lower levels of atmospheric boundary layer. Following inspiration from nature we expose a new system for angle of attack estimation based on pressure measurements on the wing. Such an equipment can be used for real-time estimation of the angle of attack during flight or even further building of wind velocity vector with additional equipment. Those information can find purpose in control and stabilization of the aircraft due to inequalities seen by the wing or even for various soaring strategies that rely on active control for energy extraction. In that purpose, flying wing aircraft has been used with totally four span-wise locations for local angle of attack estimation. In-flight angle of attack estimation from differential pressure measurements on the wing has been compared with magnetic sensor with wind vane. The results have shown that pressure ports give more reliable estimation of angle of attack when compared to values given by wind vane attached to a specially designed air-boom. Difference in local angle of attack at four span-wise locations has confirmed spatial variation of turbulence in low altitude flight. Moreover, theoretical law of energy dissipation for wind components described by Kaimal spectrum has shown acceptable match with estimated ones.

Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data

Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield. Attempts have been made to improve the yaw alignment with advanced measurement equipment but most of these techniques introduce additional costs and rely on alignment tolerances with the rotor axis or the true north. Turbines that are well aligned after commissioning may suffer an alignment degradation during their operational lifetime. Such changes need to be detected as soon as possible to minimize power losses. The objective of this paper is to propose a three-step methodology to improve turbine alignment and detect changes during operational lifetime with standard nacelle metrology (met) mast instruments (here: two cup anemometer and one wind vane). In step one, a reference turbine and an external undisturbed reference wind signal, e.g., met mast or lidar are used to determine flow corrections for the nacelle wind direction instruments to obtain a turbine alignment with optimal power production. Secondly a nacelle wind speed correction enables the application of the previous step without additional external measurement equipment. Step three is a monitoring application and allows the detection of alignment changes on the wind direction measurement device by means of a flow equilibrium between the two anemometers behind the rotor. The three steps are demonstrated at two 2 MW turbines together with a ground-based lidar. A first-order multilinear regression model gives sufficient correction of the flow distortion behind the rotor for our purposes and two wind vane alignment changes are detected with an accuracy of ±1.4∘ within 3 days of operation after the change is introduced. We show that standard turbine equipment is able to align a turbine with sufficient accuracy and changes to its alignment can be detected in a reasonably short time, which helps to minimize power losses.

Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data

Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield. Attempts have been made to improve the yaw alignment with advanced measurement equipment but most of these techniques introduce additional costs and rely on alignment tolerances with the rotor axis or the true north. Turbines that are well aligned after commissioning, may suffer an alignment degradation during their operational lifetime. Such changes need to be detected as soon as possible to minimize power losses. The objective of this paper is to propose a three-step methodology to improve turbine alignment and detect changes during operational lifetime with standard nacelle metrology (met) mast instruments (here: two cup anemometer and one wind vane). In step one, a reference turbine and an external undisturbed reference wind signal, e.g. met mast or lidar are used to determine flow corrections for the nacelle wind direction instruments to obtain a turbine alignment with optimal power production. Secondly a nacelle wind speed correction is enabling the application of the previous step without additional external measurement equipment. Step three is a monitoring application and allows to detect alignment changes on the wind direction measurement device by means of a flow equilibrium between the two anemometers behind the rotor. The three steps are demonstrated at two 2 MW turbines together with a ground based lidar. A first order multi linear regression model gives sufficient correction of the flow distortion behind the rotor for our purposes and two wind vane alignment changes are detected with an accuracy of ±1.4 ° within three days of operation after the change is introduced. We could show, that standard turbine equipment is able to align a turbine with sufficient accuracy and changes to its alignment can be detected in a reasonable short time which helps to minimize power losses.