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A Novel Smart Grid State Estimation Method Based on Neural Networks

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TLDR
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.
Abstract
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.

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References
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Power Flow Solution by Newton's Method

TL;DR: The ac power flow problem can be solved efficiently by Newton's method because only five iterations, each equivalent to about seven of the widely used Gauss-Seidel method are required for an exact solution.
Journal ArticleDOI

Accurate photovoltaic power forecasting models using deep LSTM-RNN

TL;DR: The use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems and offers a further reduction in the forecasting error compared with the other methods.
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