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Related Experiment Video

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm.

Jian Li¹, Shuoyu Li², Wen Zhao³

¹Metrology Center, Guangdong Power Grid Co.,Ltd., Guangzhou, 511545, China. honeyluyawahaha@163.com.

Scientific Reports

|August 22, 2024

Summary

This summary is machine-generated.

This study introduces advanced methods to accurately calculate distribution network line loss. Improved algorithms for data imputation and anomaly detection enhance the precision of power load analysis, aiding in efficient network management.

Keywords:

Data processing Fuzzy C-Means Isolated forest algorithm Line loss analysis Medium voltage distribution networks

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Area of Science:

Electrical Engineering
Data Science
Operations Research

Background:

Traditional distribution network loss analysis methods are insufficient for current development.
Backward management modes contribute to long-term distribution network losses.
Accurate power load data is crucial for effective network management.

Purpose of the Study:

To enhance the accuracy of filling missing values in power load data.
To improve outlier detection in load data for better analysis.
To develop an efficient method for calculating distribution network line loss in a big data environment.

Main Methods:

Particle Swarm Optimization (PSO) to optimize clustering centers for data imputation.
Improved Isolation Forest algorithm, using coefficient of variation, for anomaly detection.
Breadth-First Search (BFS)-based method for big data line loss calculation.

Main Results:

Enhanced Fuzzy C-Mean clustering achieved an average error of -6.35 with a standard deviation of 4.015 for missing data.
Improved Isolation Forest algorithm showed an Area Under the Curve (AUC) of 0.8586 for abnormal sample detection.
Feeder line loss rate was determined to be 7.62% with the proposed methods.

Conclusions:

The proposed techniques enable fast and accurate distribution network line loss analysis.
The methods provide a valuable guide for managing distribution network line loss.
Optimized data handling and analysis are key to improving distribution network efficiency.