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Related Concept Videos

The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

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Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the power flow program computes...
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Load-frequency control01:28

Load-frequency control

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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Power Factor Correction01:20

Power Factor Correction

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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Control of Power Flow01:30

Control of Power Flow

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There are several methods to control power flow in power systems:
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A modified weighted average algorithm for optimal reactive power dispatch considering uncertain load and renewable

Sulaiman Z Almutairi1, Mohamed Ebeed2, Ahmed T Hachemi3

  • 1Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj, 16278, Saudi Arabia. s.almutairi@psau.edu.sa.

Scientific Reports
|November 5, 2025
PubMed
Summary
This summary is machine-generated.

A new Modified Weighted Average Algorithm (MWAA) effectively solves optimal reactive power dispatch (ORPD) and stochastic ORPD (SORPD) problems. It enhances power system performance by optimizing reactive power flow with renewable energy integration.

Keywords:
ORPD solution processRenewable energyStatistical analysisUncertaintyWeighted average algorithm

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Area of Science:

  • Electrical Engineering
  • Power Systems Optimization
  • Computational Intelligence

Background:

  • Optimal Reactive Power Dispatch (ORPD) is vital for modern power system efficiency.
  • Integrating renewable energy sources like photovoltaics (PVs) and wind turbines (WTs) introduces uncertainties.
  • Stochastic ORPD (SORPD) addresses these uncertainties to maintain system stability and performance.

Purpose of the Study:

  • To propose a Modified Weighted Average Algorithm (MWAA) for solving both ORPD and SORPD.
  • To apply the MWAA to the 30-bus IEEE system, considering PV and WT integration.
  • To minimize total expected power loss (TEPL) and total expected voltage deviation (TEVD) in SORPD.

Main Methods:

  • Developed MWAA incorporating Fitness Distance Balance (FDB), Weibull flight, and Quasi-Oppositional-Based Learning (QOBL).
  • Modeled uncertainties in load demand and renewable generation using normal/lognormal and Weibull probability density functions (PDFs).
  • Validated MWAA performance on standard benchmark functions and CEC-2019 test suites.

Main Results:

  • MWAA demonstrated robust and competitive optimization capabilities.
  • The algorithm effectively solved both ORPD and SORPD problems on the 30-bus IEEE system.
  • Comparative analysis showed superior performance over existing state-of-the-art methods in accuracy and convergence.

Conclusions:

  • The proposed MWAA is a highly effective technique for optimal reactive power dispatch under uncertainties.
  • MWAA successfully integrates renewable energy sources while minimizing power loss and voltage deviation.
  • The algorithm offers a significant advancement for enhancing the performance and reliability of modern power systems.