A New Electrical Distribution Systems Uniform Scale Model-ing Approach Based on Improved Empirical Modal Decomposition

The construction of New Electrical Distribution Systems is the key to achieve the goal of carbon peaking and carbon neutrality in China, which will bring great changes and challenges to the electric power industry. New electrical distribution systems, as an important part of new electrical distribution systems, the modeling and simulation of new electrical distribution systems need more accurate and efficient simulation methods to verify. Therefore, this paper proposes a unified scale modeling method for New Electrical Distribution Systems based on Improved empirical mode decomposition (IEMD) for the problems of inconsistency in time scale, high complexity, and lack of accuracy in the traditional modeling methods for electrical distribution systems. In this paper, we firstly analyze the operation characteristics of the New Electrical Distribution Systems, clarify the transformation mechanism of different time scales under the New Electrical Distribution Systems, and then construct the unified scale modeling framework of the New Electrical Distribution Systems; secondly, taking into account the different time scales, we use IEMD to decompose, transform and reorganize the signals of the different time scales, and then complete the unified scale modeling of the New Electrical Distribution Systems; Finally, the feasibility of the proposed method is verified through the analysis of practical examples, which provides more effective theoretical and technical support for the subsequent stability analysis and control of New Electrical Distribution Systems.

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Published
2024-11-29
DOI
https://doi.org/10.1109/ei264398.2024.10990412
Primary Topic
Power Systems and Technologies
Type
article
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article

A New Electrical Distribution Systems Uniform Scale Model-ing Approach Based on Improved Empirical Modal Decomposition

Xueshun Ye, Jun Zhou, Dongli Jia, Xiaozhao Jin
Power Systems and Technologies
article

A New Electrical Distribution Systems Uniform Scale Model-ing Approach Based on Improved Empirical Modal Decomposition

Xueshun Ye, Jun Zhou, Dongli Jia, Xiaozhao Jin
article en

Abstract

The construction of New Electrical Distribution Systems is the key to achieve the goal of carbon peaking and carbon neutrality in China, which will bring great changes and challenges to the electric power industry. New electrical distribution systems, as an important part of new electrical distribution systems, the modeling and simulation of new electrical distribution systems need more accurate and efficient simulation methods to verify. Therefore, this paper proposes a unified scale modeling method for New Electrical Distribution Systems based on Improved empirical mode decomposition (IEMD) for the problems of inconsistency in time scale, high complexity, and lack of accuracy in the traditional modeling methods for electrical distribution systems. In this paper, we firstly analyze the operation characteristics of the New Electrical Distribution Systems, clarify the transformation mechanism of different time scales under the New Electrical Distribution Systems, and then construct the unified scale modeling framework of the New Electrical Distribution Systems; secondly, taking into account the different time scales, we use IEMD to decompose, transform and reorganize the signals of the different time scales, and then complete the unified scale modeling of the New Electrical Distribution Systems; Finally, the feasibility of the proposed method is verified through the analysis of practical examples, which provides more effective theoretical and technical support for the subsequent stability analysis and control of New Electrical Distribution Systems.

North China Electric Power University (CN), China Electric Power Research Institute
Openalex Percentile: Top 72%
Power Systems and Technologies
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A New Electrical Distribution Systems Uniform Scale Model-ing Approach Based on Improved Empirical Modal Decomposition — Xueshun Ye, Jun Zhou, et al. · (2024) | TGRS Research Map | TGRS