Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

562
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
562
Turbine-Governor Control01:17

Turbine-Governor Control

933
Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
933
Energy and Power Signals01:17

Energy and Power Signals

1.1K
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
1.1K
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

53
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
53
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.3K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.1K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Smart Energy Management for Residential PV Microgrids: ESP32-Based Indirect Control of Commercial Inverters for Enhanced Flexibility.

Sensors (Basel, Switzerland)·2025
Same author

Digital Twin Approach for Fault Diagnosis in Photovoltaic Plant DC-DC Converters.

Sensors (Basel, Switzerland)·2025
Same author

Hour-Ahead Photovoltaic Power Prediction Combining BiLSTM and Bayesian Optimization Algorithm, with Bootstrap Resampling for Interval Predictions.

Sensors (Basel, Switzerland)·2024
Same author

Announcement Signals and Automatic Braking Using Virtual Balises in Railway Transport Systems.

Sensors (Basel, Switzerland)·2022
Same author

Intra-Day Solar Power Forecasting Strategy for Managing Virtual Power Plants.

Sensors (Basel, Switzerland)·2021
Same author

Turning Base Transceiver Stations into Scalable and Controllable DC Microgrids Based on a Smart Sensing Strategy.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K

使用参数转移学习进行优化传感器数据预处理,用于风力轮机功率曲线建模.

Pedro Martín-Calzada1, Pedro Martín Sánchez1, Francisco Javier Rodríguez-Sánchez1

  • 1Department of Electronics, University of Alcalá, 28805 Alcalá de Henares, Community of Madrid, Spain.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种转移学习方法,通过优化异常检测和回归来改进风力轮机功率曲线建模. 这种方法显著减少了优化时间,并提高了模型准确性,特别是对于新轮机.

关键词:
检测异常检测异常检测优化的优化优化优化.参数转移学习是指参数转移学习.功率曲线建模的功率曲线建模传感器数据预处理传感器数据质量数据质量

更多相关视频

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

9.1K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K

相关实验视频

Last Updated: Jan 18, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K
Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

9.1K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K

科学领域:

  • 可再生能源工程可再生能源工程
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 风力轮机功率曲线建模对于运营效率和维护至关重要.
  • 在SCADA (监督控制和数据采集) 系统中的数据异常阻碍了精确的功率曲线预测.
  • 现有的方法在数据质量问题和广泛的模型调整方面扎.

研究的目的:

  • 开发一个参数转移学习策略,用于强大的风力轮机功率曲线建模.
  • 共同优化异常检测和回归模型,以提高准确性.
  • 为了减少模型调整的计算负担,并提高对新轮机的适用性.

主要方法:

  • 一个框架结合了异常检测 (iForest,LOF,DBSCAN) 和WTPC (风力轮机功率曲线) 回归器 (MLP,RF,GP).
  • 一个多度数目标函数,用于联合优化预处理和建模.
  • 转移学习与随机搜索源域超参数探索和贝叶斯优化目标域精细化.

主要成果:

  • 与传统方法相比,优化代减少了90%.
  • 在各种轮机型号和位置上持续改进目标域性能.
  • 当源轮机和目标轮机在站点或额定功率上有所不同时,没有表现损失,相似的对有更大的收益.

结论:

  • 拟议的参数转移学习管道是实用的,模型不可知,并加速预处理和建模.
  • 它有效地保持或改善模型适配,为具有有限数据的新安装轮机提供显著的好处.
  • 该战略提高了风力轮机性能监测和预测的可靠性.