基于集成树和MoE的馈线统计线损率双层估计模型

doi:10.19595/j.cnki.1000-6753.tces.221994

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摘要统计线损率是衡量电力系统经济运行的重要指标。然而,用户用电数据采集异常、数据传输中断等因素会导致统计线损率异常或缺失,这严重阻碍了智能配电网的线损精益化管理与经济高效运行。针对馈线统计线损率合理值的估计问题,本文提出了一种基于集成树和混合专家系统（Mixture of Experts,MoE）的馈线统计线损率双层估计模型。首先,使用最大信息系数以更有效地分析统计线损率与其相关特征间的非线性关系,并采用鲁棒性强的K-Medoids聚类算法对馈线进行精细划分。然后,使用Stacking集成学习框架,基于基估计和元估计双层模型对馈线统计线损率进行两阶段估计,选用决策树和各类集成树模型作为基估计模型对统计线损率进行初步估计,将各基估计模型输出结果输入到元估计模型MoE中进行最终估计,使用方均根误差（RMSE）和平均绝对误差（MAE）来衡量模型所估计统计线损率的合理性。通过算例分析表明,与其他模型相比,本文所提馈线统计线损率双层估计模型具有更低的RMSE和MAE,对馈线统计线损率的估计效果更好。

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关键词 ：统计线损率, 线损率估计, 机器学习, 集成树, 混合专家系统

Abstract：Reducing line loss is important for power grids to save energy and achieve carbon neutrality. Statistical line loss rate is an important indicator for the refined management of line loss in the power grid. However, abnormal data collection of power consumption, interruption of data transmission and other factors lead to the abnormality or missing of statistical line loss rate. At present, the ensemble learning framework is applied to the field of line loss estimation, but models used for estimation are all machine learning models, so the estimation accuracy needs to be improved. In order to improve the accuracy of statistical line loss rate estimation, a double-layers estimation model for feeder statistical line loss rate based on tree-based ensemble learning and Mixture of Experts (MoE) is proposed.
Firstly, the maximum information coefficient (MIC) is used to effectively analyze the nonlinear relationship between the statistical line loss rate and its correlated features, so as to build a feature set of statistical line loss rate. Secondly, the feature vector of each feeder is input to the robust K-Medoids clustering algorithm to realize the fine division of feeders. Thirdly, using the Stacking integrated learning framework, the feeder statistical line loss rate is estimated in two stages based on the base estimation and meta estimation double-layer models. The decision tree, gradient boosting decision Tree (GBDT), adaptive boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), random forest and extremely randomized tree (ExtraTree) are selected as base estimation models for preliminary estimation of the statistical line loss rate, and the output results of each base estimation model are input into the meta estimation model MoE for final estimation.
A comprehensive set of experiments has been conducted on a real-world feeder statistical line loss rate dataset: 1) The MIC values of statistical line loss rate and theoretical line loss rate, total length of line, line power supply, line operation time, rated capacity of distribution transformer, operation time of distribution transformer are respectively 0.948, 0.81, 0.701, 0.672, 0.768 and 0.683, which demonstrates the high correlation between each feature and the statistical line loss rate. 2) Feature vectors are fed into the K-medoids algorithm, feeders are divided into three parts. Through clustering, the total RMSE and MAE of statistical line loss rate estimated by the proposed model are decreased by 5% and 7% respectively. 3) Compared with other models, the error distribution of proposed model is concentrated in the low error area, and the between the median value and the mean value is closer, which means the proposed model has better accuracy and stability. The comparison between the proposed model and other ensemble model which has the best performance shows that, the RMSE of each type of feeders estimated by the proposed model are reduced by 4%, 2%, 5% respectively, and the MAE of each type of feeders estimated by the proposed model are reduced by 10%, 3%, 9% respectively.
The following conclusions can be drawn from the simulation analysis: (1) The maximum information coefficient is used to verify the rationality of using the theoretical line loss rate and its related features for feeder clustering and statistical line loss rate estimation. (2) Compared with direct estimation, the estimation accuracy of statistical line loss rate can be improved by clustering feeders using K-medoids algorithm. (3) Compared with the existing ensemble estimation model, the estimation model proposed in this paper has lower RMSE and MAE, which means the statistical line loss rate estimated by the proposed model is more reasonable.

Key words： Statistical line loss rate line loss rate estimation machine learning tree-based ensemble learning mixture of experts

收稿日期: 2022-10-20

PACS:

TM744

基金资助:国家自然科学基金资助项目(U2166202）

通讯作者: 郭陆阳男,1995年生,博士研究生,研究方向为智能配电网。E-mail：guoluyang@tju.edu.cn

作者简介: 王守相男,1973年生,博士,教授,博士生导师,研究方向分布式发电、微网与智能配电网等。E-mail：sxwang@tju.edu.cn。

引用本文:

王守相, 张丙杰, 赵倩宇, 郭陆阳, 张晟. 基于集成树和MoE的馈线统计线损率双层估计模型[J]. 电工技术学报, 0, (): 112-112. Wang Shouxiang, Zhang Bingjie, Zhao Qianyu, Guo Luyang, Zhang Sheng. Double-Layers Stacking Estimation Model for Feeder Statistical Line Loss Rate Based on Tree-Based Ensemble Learning and MoE. Transactions of China Electrotechnical Society, 0, (): 112-112.

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