Machine Learning-Based Ambient Temperature Estimation Using Ultrasonic Sensor

2021 ◽  
pp. 657-668
Author(s):  
Ajit Kumar Sahoo ◽  
Siba K. Udgata
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Ultrasonic Sensor ◽  
Temperature Estimation

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

Machine learning-based digital twins reduce seasonal remapping in aeroderivative gas turbines

2021 ◽  
pp. 1-7
Author(s):  
Nick Petro ◽  
Felipe Lopez
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Pilot Site ◽  
Digital Twin ◽  
Digital Twins ◽  
Machine Learning Model ◽  
Unacceptable Behavior ◽  
External Conditions

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.

Research on the Design of Intelligent Obstacle Avoidance Car Model Based on Ultrasonic Wave

2013 ◽  
Vol 461 ◽  
pp. 531-534
Author(s):  
Yu Zhu ◽  
Zhe Xin Han ◽  
Peng Kun Sun ◽  
Tian Hao Wang ◽  
Yin Han Gao
Keyword(s):  
Ambient Temperature ◽  
Ultrasonic Wave ◽  
Obstacle Avoidance ◽  
External Environment ◽  
Ultrasonic Sensor ◽  
Design System ◽  
Intelligent Vehicle ◽  
The Core ◽  
Car Model ◽  
Model Based

Based on the characteristic of an ultrasonic sensor that can sense the external environment, we design an intelligent vehicle with obstacle avoidance function. First, we use MSP430 as the core of the design system. Second, we distribute the ultrasonic sensor evenly on the round zone of the obstacles to process the data and ambient temperature, which is transmitted from the ultrasonic receiver through the SCM (what is SCM, explain). Finally, we make it to realize the functions of intelligent controlling and avoiding obstacles automatically.

scholarly journals Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2570
Author(s):  
Christil Pasion ◽  
Torrey Wagner ◽  
Clay Koschnick ◽  
Steven Schuldt ◽  
Jada Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ambient Temperature ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Solar Panels ◽  
Power Prediction ◽  
Location Data ◽  
Learning Techniques ◽  
Prior Literature

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data.

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