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Related Experiment Videos

Distribution network risk prediction based on data mining and improved PSO fused with SVM.

Jianyi Li1, Ximing Xie2, Jiefeng Jiang2

  • 1Zhongshan Power Supply Bureau, Guangdong Power Grid Co., Ltd., Zhongshan, 528400, China. ellan.100@163.com.

Scientific Reports
|May 1, 2026
PubMed
Summary

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This study introduces a novel DM-IS model for distribution network risk prediction, enhancing accuracy with fused data mining and improved swarm intelligence. The model significantly reduces errors and operational costs in power systems.

Area of Science:

  • Electrical Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Ubiquitous Power Internet of Things (Upiot) environments generate multi-source heterogeneous data.
  • Challenges include feature redundancy and poor risk prediction accuracy with small sample sizes.

Purpose of the Study:

  • To propose a robust distribution network risk prediction method.
  • To address challenges of data complexity and small sample sizes in Upiot.

Main Methods:

  • Utilized Kernel Principal Component Analysis (KPCA) for feature reduction.
  • Employed an improved Particle Swarm Optimization (PSO) with logistic mapping for parameter optimization.
  • Developed a hybrid Support Vector Machine (SVM) model (DM-IS).
Keywords:
Data miningDistribution networkParticle swarm optimizationRisk predictionSupport vector machine

Related Experiment Videos

Main Results:

  • Achieved high-precision fitting with a median error of 0.022 under small-sample constraints.
  • Outperformed baseline models by narrowing error distribution bandwidth.
  • Reduced average fault detection time by 77.90% and O&M costs by 42.65%.

Conclusions:

  • The DM-IS model effectively overcomes premature convergence in parameter optimization.
  • Demonstrated the efficacy of multi-source heterogeneous data fusion for forecast robustness.
  • Provides quantitative support for transitioning power systems to predictive maintenance.