The Experts below are selected from a list of 819051 Experts worldwide ranked by ideXlab platform
Chris Gerada - One of the best experts on this subject based on the ideXlab platform.
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Experimental Statistical Method Predicting AC Losses on Random Windings and PWM Effect Evaluation
IEEE Transactions on Energy Conversion, 2020Co-Authors: Eraldo Preci, Giampaolo Buticchi, Giorgio Valente, Alessandro Galassini, Xin Yuan, Michele Degano, David Gerada, Chris GeradaAbstract:Nowadays, one of the challenges in transport electrification is the reduction of the components’ size and weight in order to improve the power density. This is often achieved by designing electrical machines with higher rotational speeds and excitation frequencies. In addition, the converter needs to control the machine over a wide speed range given by the mission profile. Therefore, copper losses can significantly increase due to the combination of high frequency excitation and the harmonics introduced by the converter .The winding arrangement design plays a key role in the minimization of the copper losses. This paper presents an in depth study on AC losses in random windings for high frequency motor applications. An analytical Method is compared against 2-D Finite Element (FE) simulation results. These are then compared to experimental measurements taken on a custom motorette. Importantly, in order to take into account the random positions of each strand within the machine slots, an Experimental Statistic Method (ESM) is proposed. The ESM allows to define the probability distribution which is useful to evaluate the winding copper losses at the design stage. The contribution of the Pulse Width Modulation (PWM) effect is also considered and experimentally evaluated.
Sol Ha - One of the best experts on this subject based on the ideXlab platform.
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simplified nonlinear model for the weight estimation of fpso plant topside using the Statistical Method
Ships and Offshore Structures, 2016Co-Authors: Sol Ha, Seongho Seo, Myungil Roh, Hyunkyoung ShinAbstract:The weight information of an FPSO plant, especially of the FPSO (floating, production, storage, and off-loading), is one of the important data needed to estimate the amount of production material (e.g. plates) and to determine the suitable production Method for its construction. In addition, the weight information is a key factor that affects the building cost and the production period of the FPSO plant. Although the importance of the weight has long been recognised, the weight, especially of the topside, has been roughly estimated using the existing design and production data as well as the designer's experience. To improve this task, a nonlinear simplified model for the weight estimation of the FPSO plant topside using the Statistical Method was developed in this study. To do this, various past records on the estimation of the weight of the FPSO plant were collected through literature survey, and then correlation analysis and multiple regression analysis were performed to develop a nonlinear simplified ...
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simplified model for the weight estimation of floating offshore plant using the Statistical Method
ASME 2014 33rd International Conference on Ocean Offshore and Arctic Engineering, 2014Co-Authors: Hyunkyoung Shin, Namkug Ku, Sol HaAbstract:The weight information of a floating offshore plant, such as an FPSO, is one of the important data to estimate the amount of production material and to determine the production Method for its construction. In addition, the weight information is a key factor which affects in the building cost and production period of the offshore plant. Although the importance of the weight has long been recognized, the weight has been roughly estimated by using the existing design and production data, and designer’s experience. To improve this task, a simplified model for the weight estimation of the offshore plant using the Statistical Method was developed in this study. To do this, various past records to estimate the weight of the offshore plant were collected through the literature survey, and then the correlation analysis and the multiple regression analysis were performed to develop the simplified model for the weight estimation. Finally, to evaluate the applicability of the developed model, it was applied to some examples of the weight estimation of topsides of the offshore plant. The results showed that the developed model can be applied the weight estimation process of the offshore plant at the early design stage.© 2014 ASME
Chao Chen - One of the best experts on this subject based on the ideXlab platform.
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a hybrid Statistical Method to predict wind speed and wind power
Renewable Energy, 2010Co-Authors: Hui Liu, Hongqi Tian, Chao ChenAbstract:Accurate forecasting of wind speed and wind power is important for the safety of renewable energy utilization. Compared with physical Methods, Statistical Methods are usually simpler and more suitable for small farms. Based on the Methods of wavelet and classical time series analysis, a new short-term forecasting Method is proposed. Simulation upon actual time data shows that: (1) the mean relative error in multi-step forecasting based on the proposed Method is small, which is better than classical time series Method and BP network Method; (2) the proposed Method is robust in dealing with jumping data; and (3) the proposed Method is applicable to both wind speed and wind power forecasting.
C Monstein - One of the best experts on this subject based on the ideXlab platform.
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automated detection of solar radio bursts using a Statistical Method
Solar Physics, 2019Co-Authors: Dayal Singh, Prasad Subramanian, Sasikumar K Raja, R Ramesh, C MonsteinAbstract:Radio bursts from the solar corona can provide clues to forecast space-weather hazards. After recent technology advancements, regular monitoring of radio bursts has increased and large observational datasets are produced. Hence, manual identification and classification of them is a challenging task. In this article, we describe an algorithm to automatically identify radio bursts from dynamic solar radio spectrograms using a novel Statistical Method. We use e-CALLISTO (Compound Astronomical Low Cost Low Frequency Instrument for Spectroscopy and Transportable Observatory) radio spectrometer data obtained at Gauribidanur Observatory near Bangalore in India during 2013 – 2014. We have studied the classifier performance using the receiver operating characteristics. Further, we analyze type III bursts observed in the year 2014 and find that $75\%$ of the observed bursts were below 200 MHz. Our analysis shows that the positions of flare sites, which are associated with the type III bursts with upper frequency cutoff $\gtrsim200$ MHz originate close to the solar disk center.
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automated detection of solar radio bursts using a Statistical Method
arXiv: Solar and Stellar Astrophysics, 2019Co-Authors: Dayal Singh, Prasad Subramanian, Sasikumar K Raja, R Ramesh, C MonsteinAbstract:Radio bursts from the solar corona can provide clues to forecast space weather hazards. After recent technology advancements, regular monitoring of radio bursts has increased and large observational data sets are produced. Hence, manual identification and classification of them is a challenging task. In this paper, we describe an algorithm to automatically identify radio bursts from dynamic solar radio spectrograms using a novel Statistical Method. We used e-CALLISTO radio spectrometer data observed at Gauribidanur observatory near Bangalore in India during 2013 - 2014. We have studied the classifier performance using the receiver operating characteristics. Further, we studied type III bursts observed in the year 2014 and found that $75\%$ of the observed bursts were below 200 MHz. Our analysis shows that the positions of the flare sites which are associated with the type III bursts with upper-frequency cut-off $\gtrsim 200$ MHz originate close to the solar disk center
Eraldo Preci - One of the best experts on this subject based on the ideXlab platform.
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Experimental Statistical Method Predicting AC Losses on Random Windings and PWM Effect Evaluation
IEEE Transactions on Energy Conversion, 2020Co-Authors: Eraldo Preci, Giampaolo Buticchi, Giorgio Valente, Alessandro Galassini, Xin Yuan, Michele Degano, David Gerada, Chris GeradaAbstract:Nowadays, one of the challenges in transport electrification is the reduction of the components’ size and weight in order to improve the power density. This is often achieved by designing electrical machines with higher rotational speeds and excitation frequencies. In addition, the converter needs to control the machine over a wide speed range given by the mission profile. Therefore, copper losses can significantly increase due to the combination of high frequency excitation and the harmonics introduced by the converter .The winding arrangement design plays a key role in the minimization of the copper losses. This paper presents an in depth study on AC losses in random windings for high frequency motor applications. An analytical Method is compared against 2-D Finite Element (FE) simulation results. These are then compared to experimental measurements taken on a custom motorette. Importantly, in order to take into account the random positions of each strand within the machine slots, an Experimental Statistic Method (ESM) is proposed. The ESM allows to define the probability distribution which is useful to evaluate the winding copper losses at the design stage. The contribution of the Pulse Width Modulation (PWM) effect is also considered and experimentally evaluated.
