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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Modified recurrent equation-based cubic spline interpolation for missing data recovery in phasor measurement unit

Shruthi Thangaraj1, Vik Tor Goh1, Timothy Tzen Vun Yap2

  • 1Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia.

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|December 28, 2023
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Summary

A new method significantly speeds up the recovery of missing Phasor Measurement Unit (PMU) data for smart grids. This faster approach ensures reliable grid operation by efficiently interpolating essential data points.

Keywords:
cubic splinedata pre-processingdata qualitydata recoveryinterpolationmissing dataphasor measurement unitsmart grid

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Area of Science:

  • Electrical Engineering
  • Data Science
  • Power Systems

Background:

  • Smart grid operation relies on high-quality Phasor Measurement Unit (PMU) data.
  • Missing PMU data can compromise grid stability and lead to blackouts.
  • Conventional cubic interpolation methods for missing data are computationally intensive.

Purpose of the Study:

  • To develop a more efficient method for recovering missing PMU data.
  • To improve the speed and simplicity of data interpolation for smart grids.

Main Methods:

  • A modified recurrent equation-based cubic spline interpolation procedure was developed.
  • The new method simplifies the computation of spline constants.
  • Performance was evaluated using PMU data from India, comparing RMSE and calculation time.

Main Results:

  • The modified recurrent relation method is 10 times faster than conventional cubic interpolation.
  • The proposed method demonstrates effectiveness for various missing data scenarios, including edge and continuous gaps.
  • Root Mean Square Error (RMSE) values confirm the accuracy of the proposed method.

Conclusions:

  • The novel method efficiently retrieves any number of missing PMU values at any location.
  • Minimal calculations are required, enhancing computational efficiency for smart grid data recovery.