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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

350
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
350
Line Loss01:10

Line Loss

486
The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
486
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

402
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
402
Traveling Waves: Lossless Lines01:27

Traveling Waves: Lossless Lines

456
The provided content explores the behavior of traveling waves on single-phase lossless transmission lines. It begins with a single-phase two-wire lossless transmission line of length Δx, characterized by a loop inductance LH/m and a line-to-line capacitance C F/m. These parameters result in a series inductance LΔx  and a shunt capacitance CΔx.
456
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

332
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
332
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

325
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
325

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Related Experiment Video

Updated: Jan 10, 2026

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.5K

Research on precise estimation of line loss rate probability density based on bilateral total variation filtering

Jie Zhang1, Shengchun Liu2, Chao Feng3

  • 1Electric Power Science Research Institute of State Grid Qinghai Electric Power Company, Xining, Qinghai, 810008, China. zjvecra6186603@163.com.

Scientific Reports
|November 25, 2025
PubMed
Summary

This study introduces a precise method for estimating line loss rate probability density. It uses Bilateral Total Variation (BTV) filtering to enhance power data accuracy, leading to reliable density estimations.

Keywords:
Bilateral total variationFiltering algorithmKernel density estimationLine loss ratePrecise estimationProbability density

Related Experiment Videos

Last Updated: Jan 10, 2026

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.5K

Area of Science:

  • Electrical Engineering
  • Data Science
  • Signal Processing

Background:

  • Accurate estimation of line loss rate probability density is crucial for power system management.
  • Traditional methods often struggle with noise in power data, affecting accuracy.
  • Edge information preservation is key for reliable data analysis in power systems.

Purpose of the Study:

  • To propose a precise method for line loss rate probability density estimation.
  • To enhance the accuracy of power data by effectively filtering noise.
  • To validate the proposed method's performance under various conditions.

Main Methods:

  • Utilizing the Bilateral Total Variation (BTV) filtering algorithm for noise suppression and edge preservation in power data.
  • Calculating the line loss rate using a combined improved equivalent resistance method on filtered data.
  • Applying non-parametric kernel density estimation for precise probability density results.

Main Results:

  • The BTV algorithm effectively smooths noise while maintaining critical edge information in power data.
  • The proposed method enables accurate line loss rate calculation and reliable probability density estimation.
  • High Kendall correlation coefficients (≈0.88) demonstrate the method's accuracy in reflecting true line loss rate distributions.

Conclusions:

  • The proposed BTV filtering-based method provides a precise approach for line loss rate probability density estimation.
  • This technique significantly improves power data quality, leading to more reliable analytical outcomes.
  • The method's robustness is confirmed by its high accuracy across different experimental conditions.