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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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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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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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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Energy and Power Signals01:17

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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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Related Experiment Video

Updated: Oct 18, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network.

Lina Alhmoud1, Ruba Abu Khurma2, Ala' M Al-Zoubi2,3

  • 1Department of Electrical Power Engineering, Faculty of Engineering Technology, Yarmouk University, Irbid 21163, Jordan.

Sensors (Basel, Switzerland)
|September 28, 2021
PubMed
Summary

Accurate electrical load forecasting is crucial for power system planning. This study introduces an optimized neural network using the Grey Wolf Optimizer (GWO) for precise week-ahead load predictions in Jordan, improving efficiency and reducing costs.

Keywords:
artificial neural networkhourly demandload forecastingmaximum demandtotal demand

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Operations Research

Background:

  • Effective power system planning relies on accurate load forecasting for operational efficiency, security, and cost minimization.
  • Traditional forecasting methods face challenges with data resolution and type, impacting accuracy.
  • Jordan's electricity sector requires improved forecasting to manage demand and optimize resource allocation.

Purpose of the Study:

  • To develop an accurate and efficient method for week-ahead electrical load forecasting in Jordan.
  • To quantify the benefits of enhanced data and advanced models in load forecasting.
  • To propose an optimized neural network model for precise power demand prediction.

Main Methods:

  • Utilized actual daily and hourly electrical load data from Jordan for over a year.
  • Developed an optimized multi-layered feed-forward neural network.
  • Implemented the Grey Wolf Optimizer (GWO) to enhance the neural network's performance.
  • Formulated the power forecasting problem as a minimization challenge.

Main Results:

  • The proposed GWO-optimized neural network achieved highly competitive forecasting results.
  • Demonstrated the impact of data resolution and type on forecasting accuracy.
  • The model proved effective for week-ahead electrical load forecasting based on current measurements.

Conclusions:

  • The GWO-optimized neural network offers a superior approach to electrical load forecasting in Jordan.
  • Accurate forecasting minimizes resource waste and enhances power system management.
  • This research provides a valuable tool for electrical utility planning and operation in the region.