A Novel Smart Grid State Estimation Method Based on Neural Networks

被引：26

作者：

Abdel-Nasser, Mohamed ^{[1
]}

Mahmoud, Karar ^{[1
]}

Kashef, Heba ^{[1
]}

机构：

[1] Aswan Univ, Dept Elect Engn, Aswan 81542, Egypt

来源：

INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE | 2018年 / 5卷 / 01期

关键词：

Neural Network; Smart Grid; Renewable Energy; Power Loss; Voltage Profile; POWER; PENETRATION; MODELS;

D O I：

10.9781/ijimai.2018.01.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods.

引用

页码：92 / 100

页数：9

共 31 条

[1] Accurate photovoltaic power forecasting models using deep LSTM-RNN [J].