A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation
A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation
复制标题
配电系统状态估计中伪测量生成的博弈论数据驱动方法
DOI：
--
复制
发表时间：
2018
影响因子：
9.6
通讯作者：
Fankun Bu
中科院分区：
文献类型：
--
作者：
K. Dehghanpour;Yuxuan Yuan;Zhaoyu Wang;Fankun Bu
在本文中，我们提出了一个有效的计算框架，其目的是产生加权伪测量，以提高配电系统状态估计（DSSE）的质量，并提供可观测性与先进的计量基础设施（AMI）对不可观测的客户和丢失的数据。所提出的技术是基于相关向量机（RVMs）的博弈论扩展。该平台能够估计节点功耗并量化其不确定性，同时减少大型AMI数据集模型训练的计算负担。为了实现这一目标，大型训练集被分解并分布在多个并行学习实体之间。从并行RVM的估计结果，然后结合使用基于重复游戏的想法与向量回报的博弈论模型。据观察，通过这种方法并利用客户行为的季节性变化，可以大大提高伪测量的准确性，同时引入针对不良训练数据样本的鲁棒性。建议的伪测量生成模型集成到DSSE使用闭环信息系统，它利用分支当前状态估计器（BCSE），以进一步提高所设计的机器学习框架的性能。该方法已在一个实际的配电馈线模型与智能电表数据进行了验证。
In this paper, we present an efficient computational framework with the purpose of generating weighted pseudo-measurements to improve the quality of distribution system state estimation (DSSE) and provide observability with advanced metering infrastructure (AMI) against unobservable customers and missing data. The proposed technique is based on a game-theoretic expansion of relevance vector machines (RVMs). This platform is able to estimate the nodal power consumption and quantify its uncertainty while reducing the prohibitive computational burden of model training for large AMI datasets. To achieve this objective, the large training set is decomposed and distributed among multiple parallel learning entities. The resulting estimations from the parallel RVMs are then combined using a game-theoretic model based on the idea of repeated games with vector payoff. It is observed that through this approach and by exploiting the seasonal changes in customers’ behavior the accuracy of pseudo-measurements can be considerably improved, while introducing robustness against bad training data samples. The proposed pseudo-measurement generation model is integrated into a DSSE using a closed-loop information system, which takes advantage of a branch current state estimator (BCSE) to further improve the performance of the designed machine learning framework. This method has been tested on a practical distribution feeder model with smart meter data for verification.
DOI：
10.1109/tcsii.2018.2796938
发表时间：
2018-01
期刊：
IEEE Transactions on Circuits and Systems II: Express Briefs
影响因子：
--
作者：
N. Bretas;A. Bretas
通讯作者：
N. Bretas;A. Bretas
DOI：
10.1016/j.ijepes.2018.06.039
发表时间：
2019
影响因子：
5.2
作者：
A. Bretas;N. Bretas;B. E. Carvalho
通讯作者：
A. Bretas;N. Bretas;B. E. Carvalho