Tri-objective enhanced ISODATA: a synergistic framework of cluster core optimization, inter-class divergence maximization, and adaptive threshold control for smart grid load profiling

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research Pub Date : 2025-05-08 DOI:10.1016/j.epsr.2025.111774

Xu Tian, Bingsheng Yuan, Pengxiang Zheng

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引用次数: 0

Abstract

This paper innovatively proposes an improved ISODATA algorithm (IM-ISODATA) aimed at enhancing the accuracy and adaptability of load pattern clustering in power systems. Specifically, the algorithm initially employs a farthest-first probability initialization strategy to balance global search capability with computational efficiency. It is followed by a dynamic distance optimization framework that refines cluster structures and incorporates an adaptive parameter tuning mechanism to dynamically align with load variations. Extensive experiments demonstrate significant advancements: the HACIM strategy reduces the IDB by 18.7 % and increases the ICH by 26.5 %, yielding optimal adaptive parameters. Compared with traditional algorithms, IM-ISODATA achieves the lowest IDB and highest ICH, with a 98.11 % improvement in computational efficiency. In microgrid scenarios, the algorithm attains an 81.5 % Pattern Recognition Accuracy (PRA), representing a 5.7 % improvement, while in demand response scenarios, it achieves a 76.2 % Demand Response Matching rate (DRM), reflecting a 7.1 % enhancement. In conclusion, the adaptive mechanism and computational efficiency of IM-ISODATA facilitate precise load pattern recognition for dynamic demand response management.

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三目标增强型ISODATA：集群核心优化、类间分歧最大化和智能电网负载分析自适应阈值控制的协同框架

本文创新性地提出了一种改进的ISODATA算法（IM-ISODATA），旨在提高电力系统负荷模式聚类的准确性和自适应性。具体而言，该算法最初采用了最远优先的概率初始化策略，以平衡全局搜索能力和计算效率。其次是一个动态距离优化框架，细化集群结构，并结合自适应参数调整机制，以动态对齐负载变化。大量的实验证明了显著的进步：HACIM策略减少了18.7%的IDB，增加了26.5%的ICH，产生了最优的自适应参数。与传统算法相比，IM-ISODATA实现了最低的IDB和最高的ICH，计算效率提高了98.11%。在微电网场景中，该算法实现了81.5%的模式识别准确率（PRA），提高了5.7%，而在需求响应场景中，该算法实现了76.2%的需求响应匹配率（DRM），提高了7.1%。综上所述，IM-ISODATA的自适应机制和计算效率为动态需求响应管理提供了精确的负荷模式识别。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Electric Power Systems Research 工程技术-工程：电子与电气

CiteScore

7.50

自引率

17.90%

发文量

963

审稿时长

3.8 months

期刊介绍： Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview. • Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation. • Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design. • Substation work: equipment design, protection and control systems. • Distribution techniques, equipment development, and smart grids. • The utilization area from energy efficiency to distributed load levelling techniques. • Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.