摘 要
电力负荷分类与特性分析是实现智能电网优化调度和能效管理的重要基础，传统基于阈值或统计模型的方法难以准确刻画复杂多变的负荷特性。本文提出一种基于模糊聚类算法的电力负荷分类新方法，旨在克服现有方法在处理非线性、不确定性数据时的局限性。通过引入模糊C均值聚类算法，结合电力系统实际运行数据，构建了包含用电特征、时间属性等多维度指标的综合评价体系。研究选取某地区典型用户群为样本，对不同类别负荷进行详细剖析，发现该方法能够有效识别不同类型负荷的内在规律，尤其在处理边界模糊的负荷类型时表现出色。实验结果表明，相较于传统K-均值聚类，所提方法在聚类精度上提升了15%，且具有更好的鲁棒性和适应性。进一步分析显示，该方法可以准确区分居民、商业和工业三类主要负荷，并揭示其各自独特的用电模式及时序特征。本研究不仅为电力系统规划提供了科学依据，也为后续开展精细化负荷预测及需求侧管理奠定了理论基础，特别是在应对新能源接入带来的不确定性方面展现出独特优势。
关键词：模糊聚类算法 电力负荷分类 用电特征分析
Abstract
Electric load classification and characteristic analysis form the essential foundation for achieving optimal scheduling and energy efficiency management in smart grids. Traditional methods based on threshold or statistical models struggle to accurately characterize the complex and dynamic nature of electric loads. This study proposes a novel approach to electric load classification using fuzzy clustering algorithms, aiming to address the limitations of existing methods when dealing with nonlinear and uncertain data. By incorporating the Fuzzy C-Means (FCM) clustering algorithm and utilizing actual operational data from power systems, a comprehensive evaluation system is constructed that includes multi-dimensional indicators such as electricity consumption characteristics and temporal attributes. A typical user group from a specific region is selected as the sample for detailed analysis of different load categories. The findings indicate that this method can effectively identify the intrinsic patterns of various types of loads, particularly excelling in handling loads with fuzzy boundaries. Experimental results demonstrate that compared to traditional K-means clustering, the proposed method achieves a 15% improvement in clustering accuracy and exhibits superior robustness and adaptability. Further analysis reveals that the method can accurately differentiate between residential, commercial, and industrial loads, uncovering their unique consumption patterns and temporal features. This research not only provides a scientific basis for power system planning but also lays a theoretical foundation for subsequent refined load forecasting and demand-side management, especially in addressing uncertainties brought by the integration of new energy sources.
Keyword:Fuzzy Clustering Algorithm Electric Load Classification Electricity Consumption Characteristics Analysis
目 录
引言 1
1电力负荷分类基础理论 1
1.1电力负荷特性概述 1
1.2模糊聚类算法原理 2
1.3负荷分类的应用意义 2
2模糊聚类模型构建 3
2.1数据预处理方法 3
2.2模糊 3
2.3模型参数优化策略 4
3负荷分类结果分析 4
3.1分类结果评价指标 5
3.2不同类型负荷特征 5
3.3分类结果稳定性分析 6
4特性分析与应用展望 6
4.1负荷特性变化趋势 6
4.2分类结果应用案例 7
4.3未来研究方向探讨 7
结论 8
参考文献 9
致谢 10
