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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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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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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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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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Related Experiment Video

Updated: Sep 26, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Ship power load forecasting based on PSO-SVM.

Xiaoqiang Dai1,2, Kuicheng Sheng3, Fangzhou Shu1

  • 1School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China.

Mathematical Biosciences and Engineering : MBE
|April 18, 2022
PubMed
Summary
This summary is machine-generated.

Accurate ship power load forecasting is crucial for system stability. An improved particle swarm optimization support vector machine (IPSO-SVM) algorithm enhances prediction accuracy by optimizing key parameters.

Keywords:
improved particle swarm optimization support vector machineload forecastingship power systemsupport vector machine

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Area of Science:

  • Marine engineering
  • Electrical engineering
  • Artificial intelligence

Background:

  • Ship power systems have limited capacity and unpredictable loads, unlike land grids.
  • Accurate ship power load forecasting is vital for operational safety and stability.
  • Support Vector Machine (SVM) is a common algorithm for load forecasting.

Purpose of the Study:

  • To enhance ship power load forecasting accuracy.
  • To improve the performance of ship energy management systems.
  • To develop an optimized SVM algorithm for ship power load prediction.

Main Methods:

  • Utilized water flow velocity, wind speed, and ship speed as input features for SVM.
  • Implemented an improved particle swarm optimization (IPSO) algorithm to tune SVM parameters (C and σ).
  • Developed a novel IPSO-SVM algorithm for real-time parameter optimization.

Main Results:

  • The IPSO-SVM algorithm demonstrated reduced load forecasting errors.
  • Prediction accuracy for ship power load was significantly improved.
  • Enhanced energy management system performance was observed.

Conclusions:

  • The IPSO-SVM algorithm offers a robust solution for accurate ship power load forecasting.
  • Optimizing SVM parameters with IPSO is effective in improving prediction accuracy.
  • This approach contributes to safer and more efficient ship power system operations.