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Related Concept Videos

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

209
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...
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Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
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z Scores and Area Under the Curve01:17

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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Choosing Between z and t Distribution01:25

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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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z Scores and Unusual Values01:07

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The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
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Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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  1. Home
  3. Research Domains
  5. Engineering
  7. Geomatic Engineering
  9. Surveying (incl. Hydrographic Surveying)
  11. Iterative Rolling Difference-z-score And Machine Learning Imputation For Wind Turbine Foundation Monitoring.
  1. Home
  3. Research Domains
  5. Engineering
  7. Geomatic Engineering
  9. Surveying (incl. Hydrographic Surveying)
  11. Iterative Rolling Difference-z-score And Machine Learning Imputation For Wind Turbine Foundation Monitoring.

Related Experiment Video

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

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Iterative rolling difference-Z-score and machine learning imputation for wind turbine foundation monitoring.

Renjie Li1, Xiangxing Lu1, Jizhang Zhao2,3,4

  • 1Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan, China.

Plos One
|September 5, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new method to detect anomalies and fill missing data in wind farm monitoring. It ensures accurate structural health assessment for renewable energy infrastructure.

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Area of Science:

  • Engineering
  • Data Science
  • Renewable Energy Systems

Background:

  • Structural health monitoring (SHM) is crucial for engineering structures, especially in renewable energy.
  • On-site data acquisition faces challenges like equipment instability and environmental complexity, leading to data anomalies and gaps.
  • Accurate performance evaluation of structures like wind farms relies heavily on precise, complete monitoring data.

Purpose of the Study:

  • To address data anomalies and gaps in wind farm monitoring data (weld nail strain, anchor cable axial force, concrete strain).
  • To develop and validate robust methods for anomaly detection and data imputation in challenging on-site environments.
  • To enhance the reliability and integrity of structural monitoring data for long-term safety assessments.

Main Methods:

  • Proposed an iterative rolling difference-Z-score method for effective anomaly detection.
  • Developed a machine learning imputation framework combining linear interpolation and LightGBM for data reconstruction.
  • Conducted experiments on real-world data from a wind farm reinforcement project in Shandong Province.

Main Results:

  • The iterative rolling difference-Z-score method demonstrated robust anomaly detection, even with up to 80% data loss.
  • The imputation framework achieved low Mean Squared Error (MSE) of 0.0214-0.0227 and Root Mean Squared Error (RMSE) of 0.14-0.15 for continuous missing data.
  • Reliable data reconstruction was achieved for up to 50% data loss in continuous missing data scenarios.

Conclusions:

  • The developed methods provide a reliable solution for improving the quality of wind farm monitoring data.
  • Enhanced data integrity supports more accurate structural condition assessment and safety evaluations.
  • This research contributes to the long-term structural reliability of renewable energy infrastructure.