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Related Concept Videos

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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Fast Decoupled and DC Powerflow01:24

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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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Maximum Power Flow and Line Loadability01:23

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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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Generator Voltage Control01:21

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Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
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Control of Power Flow01:30

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There are several methods to control power flow in power systems:
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The Power Flow Problem and Solution01:26

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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...
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Related Experiment Video

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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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An intelligent scheduling control method for smart grid based on deep learning.

Zhanying Tong1, Yingying Zhou1, Ke Xu2

  • 1School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang, 453003, China.

Mathematical Biosciences and Engineering : MBE
|May 10, 2023
PubMed
Summary

This study introduces a deep learning method for smart grid power scheduling, using long short-term memory and particle swarm optimization (PSO) to manage big data challenges and optimize energy use.

Keywords:
PSO optimizationdeep learningintelligent computingscheduling controlsmart grid

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Area of Science:

  • Smart Grid Technology
  • Artificial Intelligence in Energy Systems
  • Computational Intelligence

Background:

  • Smart grid power scheduling faces challenges due to massive data increases.
  • Traditional methods struggle with the scale and complexity of grid data.
  • Efficient data analysis is crucial for effective power scheduling.

Purpose of the Study:

  • To propose an intelligent scheduling control method for smart grids using deep learning.
  • To address the challenges posed by large-scale big data in grid operations.
  • To optimize energy savings and reduce emissions in real-time power generation.

Main Methods:

  • Utilizing deep learning, specifically the long short-term memory (LSTM) algorithm, to model historical data and extract features.
  • Employing the particle swarm optimization (PSO) algorithm for generating control decisions and optimizing scheduling.
  • Developing an intelligent power scheduling algorithm integrating deep learning and PSO.

Main Results:

  • The LSTM algorithm effectively predicts unit coal consumption based on historical data.
  • The PSO-based algorithm achieves energy savings and emission reductions while meeting power demands.
  • Experimental results on a real-world smart grid dataset demonstrate good performance.

Conclusions:

  • The proposed deep learning-based intelligent scheduling control method is effective for smart grids.
  • The integration of LSTM and PSO offers a robust solution for big data challenges in power scheduling.
  • This approach contributes to more efficient and sustainable smart grid operations.