scholarly journals Inverse Thermal Identification of a Thermally Instrumented Induction Machine Using a Lumped-Parameter Thermal Model

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 37 ◽  
Author(s):  
Pieter Nguyen Phuc ◽  
Hendrik Vansompel ◽  
Dimitar Bozalakov ◽  
Kurt Stockman ◽  
Guillaume Crevecoeur
Keyword(s):  
Motor Control ◽  
Real Time ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Thermal Model ◽  
Computation Time ◽  
Absolute Error ◽  
Thermal Parameter ◽  
Maximum Absolute Error ◽  
Lumped Parameter

Accurate temperature estimation inside an electrical motor is key for condition monitoring, fault detection, and enhanced end-of-life duration. Additionally, thermal information can benefit motor control to improve operational performance. Lumped-parameter thermal networks (LPTNs) for electrical machines are both flexible and cost-effective in computation time, which makes them attractive for use in real-time condition monitoring and integration in motor control. However, the accuracy of these thermal networks heavily depends on the accuracy of its system parameters, some of which are difficult to calculate analytically or even empirically and need to be determined experimentally. In this paper, a methodology for the thermal condition monitoring of long-duration transient and steady-state temperatures in an induction motor is presented. To achieve this goal, a computationally efficient second-order LPTN for a 5.5 kW squirrel-cage induction motor is proposed to apprehend the dominant heat paths. A fully thermally instrumented induction motor has been prepared to collect spatial and temporal temperature information. Using the experimental stator and rotor temperature data collected at different motor operating speeds and torques, the key thermal parameter values in the LPTN are identified by means of an inverse methodology that aligns the simulated temperatures of the stator windings and rotor with the corresponding measured temperatures. Validation results show that the absolute average thermal modelling error does not exceed 1.45 °C with maximum absolute error of 2.10 °C when the motor operates at fixed speed and torque. During intermittent motor-loading operation, a mean (maximum) stator temperature error of 0.38 °C (0.92 °C) was achieved and mean (maximum) rotor errors of 2.11 °C (3.40 °C). These results show the validity of the proposed thermal model but also its ability to predict in real time the temperature variations in stator and rotor for condition monitoring and motor control.

scholarly journals PREPRINT: Using digital epidemiology methods to monitor influenza-like illness in the Netherlands in real-time: the 2017-2018 season

2018 ◽  
Author(s):  
PP Schneider ◽  
CJAW van Gool ◽  
P Spreeuwenberg ◽  
M Hooiveld ◽  
GA Donker ◽  
...  
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Search Engine ◽  
Prediction Models ◽  
Absolute Error ◽  
Search Query ◽  
Maximum Absolute Error ◽  
Influenza Like Illness ◽  
Digital Epidemiology ◽  
Epidemiology Methods

AbstractIntroductionDespite the early development of Google Flu Trends in 2009, digital epidemiology methods have not been adopted widely, with most research focusing on the USA. In this article we demonstrate the prediction of real-time trends in influenza-like illness (ILI) in the Netherlands using search engine query data.MethodsWe used flu-related search query data from Google Trends in combination with traditional surveillance data from 40 general sentinel practices to build our predictive models. We introduced an artificial 4-week delay in the use of GP data in the models, in order to test the predictive performance of the search engine data.Simulating the weekly use of a prediction model across the 2017/2018 flu season we used lasso regression to fit 52 prediction models (one for each week) for weekly ILI incidence. We used rolling forecast cross-validation for lambda optimization in each model, minimizing the maximum absolute error.ResultsThe models accurately predicted the number of ILI cases during the 2017/18 ILI epidemic in real time with a mean absolute error of 1.40 (per 10,000 population) and a maximum absolute error of 6.36. The model would also have identified the onset, peak, and end of the epidemic with reasonable accuracyThe number of predictors that were retained in the prediction models was small, ranging from 3 to 5, with a single keyword (‘Griep’ = ‘Flu’) having by far the most weight in all models.DiscussionThis study demonstrates the feasibility of accurate real-time ILI incidence predictions in the Netherlands using internet search query data. Digital ILI monitoring strategies may be useful in countries with poor surveillance systems, or for monitoring emergent diseases, including influenza pandemics. We hope that this transparent and accessible case study inspires and supports further developments in field of digital epidemiology in Europe and beyond.

scholarly journals Next Generation Network Real-Time Kinematic Interpolation Segment to Improve the User Accuracy

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Mohammed Ouassou ◽  
Anna B. O. Jensen ◽  
Jon G. O. Gjevestad ◽  
Oddgeir Kristiansen
Keyword(s):  
Real Time ◽  
Mean Square Error ◽  
Thin Plate ◽  
Goodness Of Fit ◽  
Spline Interpolation ◽  
Computation Time ◽  
Absolute Error ◽  
Kriging Interpolation ◽  
Mean Square ◽  
Real Time Kinematic

This paper demonstrates that automatic selection of the right interpolation/smoothing method in a GNSS-based network real-time kinematic (NRTK) interpolation segment can improve the accuracy of the rover position estimates and also the processing time in the NRTK processing center. The methods discussed and investigated are inverse distance weighting (IDW); bilinear and bicubic spline interpolation; kriging interpolation; thin-plate splines; and numerical approximation methods for spatial processes. The methods are implemented and tested using GNSS data from reference stations in the Norwegian network RTK service called CPOS. Data sets with an average baseline between reference stations of 60–70 km were selected. 12 prediction locations were used to analyze the performance of the interpolation methods by computing and comparing different measures of the goodness of fit such as the root mean square error (RMSE), mean square error, and mean absolute error, and also the computation time was compared. Results of the tests show that ordinary kriging with the Matérn covariance function clearly provides the best results. The thin-plate spline provides the second best results of the methods selected and with the test data used.

Real Time Implementation of High Performance’s Direct Torque Control of Induction Motor on FPGA

2014 ◽  
Vol 9 (5) ◽  
pp. 919 ◽  
Author(s):  
Saber Krim ◽  
Soufien Gdaim ◽  
Abdellatif Mtibaa ◽  
Mohamed Faouzi Mimouni
Keyword(s):  
Real Time ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control
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