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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.
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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...
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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:
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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...
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Energy Losses in Transformers01:21

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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.
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Related Experiment Video

Updated: Jan 18, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Optimized Sensor Data Preprocessing Using Parameter-Transfer Learning for Wind Turbine Power Curve Modeling.

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
Summary
This summary is machine-generated.

This study introduces a transfer learning method to improve wind turbine power curve modeling by optimizing anomaly detection and regression. The approach significantly reduces optimization time and enhances model accuracy, especially for new turbines.

Keywords:
anomaly detectionoptimizationparameter-transfer learningpower curve modelingsensor data preprocessingsensor data quality

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Area of Science:

  • Renewable Energy Engineering
  • Data Science
  • Machine Learning

Background:

  • Wind turbine power curve modeling is crucial for operational efficiency and maintenance.
  • Data anomalies in SCADA (Supervisory Control and Data Acquisition) systems hinder accurate power curve predictions.
  • Existing methods struggle with data quality issues and extensive model tuning.

Purpose of the Study:

  • To develop a parameter-transfer learning strategy for robust wind turbine power curve modeling.
  • To jointly optimize anomaly detection and regression models for improved accuracy.
  • To reduce the computational burden of model tuning and enhance applicability to new turbines.

Main Methods:

  • A framework combining anomaly detection (iForest, LOF, DBSCAN) and WTPC (Wind Turbine Power Curve) regressors (MLP, RF, GP).
  • A multi-metric objective function for joint optimization of preprocessing and modeling.
  • Transfer learning with randomized search for source domain hyperparameter exploration and Bayesian optimization for target domain refinement.

Main Results:

  • Achieved a 90% reduction in optimization iterations compared to traditional methods.
  • Consistently improved target domain performance across various turbine models and locations.
  • Demonstrated no performance loss when source and target turbines differed in site or rated power, with larger gains for similar pairs.

Conclusions:

  • The proposed parameter-transfer learning pipeline is practical, model-agnostic, and accelerates preprocessing and modeling.
  • It effectively preserves or improves model fit, offering significant benefits for newly installed turbines with limited data.
  • The strategy enhances the reliability of wind turbine performance monitoring and forecasting.