scholarly journals Penampil Gelombang Tegangan dan Arus Berbasis Arduino Due untuk Generator AC Tiga Fasa

2020 ◽  
Vol 8 (2) ◽  
pp. 336
Author(s):  
MARTANTO MARTANTO ◽  
RB DWISENO WIHADI ◽  
RONNY DWI AGUSULISTYO ◽  
TJENDRO TJENDRO
Keyword(s):  
Current Sensor ◽  
Average Error ◽  
Current Output ◽  
Three Phase ◽  
Sensor Current ◽  
The Common ◽  
Permanent Magnet Generators ◽  
Python Programming ◽  
Signal Conditioning Circuit ◽  
Maximum Minimum

ABSTRAKDalam pengembangan generator tiga fasa magnet permanen diperlukan pengukuran besaran-besaran untuk melihat karakteristik generator. Besaran yang biasanya diukur adalah tegangan, arus, dan daya, namun bentuk gelombang keluaran tegangan dan arus tiap fasa kurang diperhatikan apakah sinus atau tidak. Maka perlu dirancang sebuah sistem yang bisa menampilkan bentuk gelombang tegangan dan arus sekaligus. Sistem ini diimplementasikan menggunakan sensor tegangan, sensor arus, rangkaian pengondisi sinyal, Arduino Due, dan komputer sebagai penampil menggunakan bahasa Python. Hasil pengujian diperoleh bahwa sistem bisa menampilkan bentuk gelombang keluaran tegangan dan arus, menampilkan nilai maksimum, minimum, rerata, dan rms. Nilai galat rata-rata untuk ketiga pengukuran tegangan adalah 1%, dan untuk pengukuran arus adalah 3,15%.Kata kunci: gelombang tegangan dan arus, Arduino Due, Python, tiga fasa ABSTRACTThe development of three phase permanent magnet generators require the measurement of related quantities to determine the characteristics of generator. The common measured quantities are voltage, current, and power. However the voltage and current output waveforms of each phase are not considered. Therefore a system is designed which is able to display voltage and current waveforms at once. This system is implemented using a voltage sensor, current sensor, signal conditioning circuit, Arduino Due, and a computer as a GUI using the Python programming language. The results of implementation and testing show that the GUI is able to display the voltage and current output waveforms, in addition, performs the maximum, minimum, average, and rms values. The average error value for the three voltage measurements is 1%, and for the three current measurements is 3.15%.Keywords: voltage and current waveforms, Arduino Due, Python, three phases

scholarly journals Opposite Triangle Carrier with SVPWM for Common-Mode Voltage Reduction in Dual Three Phase Motor Drives

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 282
Author(s):  
Seon-Ik Hwang ◽  
Jang-Mok Kim
Keyword(s):  
Electromagnetic Interference ◽  
Pulse Width Modulation ◽  
Motor Drives ◽  
Common Mode ◽  
Pwm Inverter ◽  
Common Mode Voltage ◽  
Voltage Vector ◽  
Three Phase ◽  
The Common ◽  
Zero Voltage

The common-mode voltage (CMV) generated by the switching operation of the pulse width modulation (PWM) inverter leads to bearing failure and electromagnetic interference (EMI) noises. To reduce the CMV, it is necessary to reduce the magnitude of dv/dt and change the frequency of the CMV. In this paper, the range of the CMV is reduced by using opposite triangle carrier for ABC and XYZ winding group, and the change in frequency in the CMV is reduced by equalizing the dwell time of the zero voltage vector on ABC and XYZ winding group of dual three phase motor.

scholarly journals Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Runzhi Zhang ◽  
Alejandro R. Walker ◽  
Susmita Datta
Keyword(s):  
Machine Learning ◽  
Error Rates ◽  
Average Error ◽  
Importance Score ◽  
Learning Methods ◽  
Principal Coordinates Analysis ◽  
Principal Coordinates ◽  
Total Variability ◽  
The Common ◽  
Feature Selecting

Abstract Background Composition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selecting, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets. Results Features selecting, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.93 and 30.37% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively. Conclusions The results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which could be used to identify the sample origins. This was also supported by the results from ANCOM and importance score from the RF. In addition, the accuracy of the prediction could be improved by more samples and better sequencing depth.

Three-Phase and Five-Phase Motor Windings Common Point Potential Ripple with Respect to the Converter Zero Termina

Vestnik MEI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 51-59
Author(s):  
Vladimir M. Tereshkin ◽  
◽  
Irshat L. Aitov ◽  
Dmitriy A. Grishin ◽  
Vyacheslav V. Tereshkin ◽  
...  
Keyword(s):  
Amplitude Modulation ◽  
Zero Point ◽  
Space Time ◽  
Common Point ◽  
Point Potential ◽  
Voltage Vector ◽  
Three Phase ◽  
Ripple Amplitude ◽  
The Common ◽  
Rotating Field

The aim of the study is to determine the parameters characterizing the ripple of a motor's three- and five-phase windings common point potentials (for the star winding connection diagram) with respect to the converter zero point. One of the reserves for decreasing electromagnetically induced vibration of an electric motor with a rotating field is to increase the number of working winding phases. The study subject is a five-phase motor winding connected to a bridge converter, namely, its ability to reduce electromagnetically induced vibration in comparison with that in using a three-phase winding. The common point potential ripple parameters are studied, and an approach is proposed to estimating the amplitude modulation of the space-time voltage vector of three- and five-phase windings under the influence of the common point potential ripple with respect to the converter zero point. Theoretical studies were carried out using the Fourier series expansion method and vector analysis methods. To confirm the theoretical results, experimental studies of the prototypes of three-phase and five-phase synchronous motors with inductors made on the basis of permanent magnets were carried out. The main results have shown the following. With increasing the number of phases of the rotating field motor working winding connected to a bridge converter, the common point potential ripple amplitude with respect to the converter zero point decreases, and the ripple frequency increases. The product of ripple amplitude by frequency remains unchanged. It is assumed that the common point potential ripple of the motor multiphase winding with respect to the converter zero terminal results in the amplitude modulation of the space-time voltage vector. With increasing the number of winding phases, the modulation amplitude decreases, and the modulation frequency increases. A five-phase motor has a lower level of the working winding common point potential ripple with respect to the converter zero point in comparison with a three-phase motor. Thus, it can be assumed that there will be a lower level of electromagnetically induced vibration in using a simple converter operation algorithm. The obtained results can be used in designing electric traction systems with vector control on the basis of multiphase motors. With increasing the number of phases, the common point potential ripple amplitude in a multiphase winding with respect to the converter zero point decreases, and the ripple frequency increases. Thus, the common point potential ripple amplitude in a five-phase winding is 5/3 times less than that in a three-phase winding, and the ripple frequency increases by 5/3 times, respectively. With increasing the number of working winding phases, the amplitude modulation of the resulting space-time voltage vector decreases. This circumstance has a positive effect on decreasing the electromagnetically induced vibration.

scholarly journals Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge

2020 ◽  
Author(s):  
Runzhi Zhang ◽  
Alejandro R. Walker ◽  
Susmita Datta
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Error Rates ◽  
Average Error ◽  
Importance Score ◽  
Learning Methods ◽  
Principal Coordinates Analysis ◽  
Principal Coordinates ◽  
Total Variability ◽  
The Common

Abstract BackgroundComposition of microbial communities can be location specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selection, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets.ResultsFeature selection, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.6% and 30.0% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively.ConclusionsThe results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which was also supported by the results from ANCOM and importance score from the RF. The analysis utilized in this study can be of great help in field of forensic science to efficiently predict the origin of the samples. And the accurate of the prediction could be improved by more samples and better sequencing depth.

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