Decentralized State Estimation for Distribution Systems using Artificial Neural Network

被引：0

作者：

Chen, Yan ^{[1
]}

Fadda, Maria G. ^{[1
]}

Benigni, Andrea ^{[1
]}

机构：

[1] Univ South Carolina, Dept Elect Engn, Columbia, SC 29208 USA

来源：

2018 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC): DISCOVERING NEW HORIZONS IN INSTRUMENTATION AND MEASUREMENT | 2018年

基金：

美国国家科学基金会;

关键词：

Artificial neural networks; mutual information; renewable energy sources; state estimation;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper we present a decentralized state estimation method to support real-time Volt/Var optimization in distribution network with high penetration of distributed generation. The network is divided into sub-areas according to the location of the generation units and the mutual information (MI) between the states of interest and the available measurements. The proposed decentralized state estimation scheme only relies on local information and on a limited amount of information from neighboring areas. In each area, an artificial neural network (ANN) is used to estimate the loads consumptions. The proposed approach is tested using a modified IEEE 34-node test feeder. The effectiveness of the method is validated on a Hardware-In-the-Loop (HIL) simulation platform. To evaluate the accuracy and efficiency of the proposed decentralized approach we compared the results obtained to a centralized and a totally local approach.

引用

页码：1342 / 1347

页数：6

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