Power Transformer Forecasting in Smart Grids Using NARX Neural Networks

2020 ◽  
pp. 401-414
Author(s):  
J. Ramírez ◽  
F. J. Martínez-Murcia ◽  
F. Segovia ◽  
A. Ortiz ◽  
D. Salas-González ◽  
...  
Keyword(s):  
Neural Networks ◽  
Smart Grids ◽  
Power Transformer

Real-Time Smart Grids Control for Preventing Cascading Failures and Blackout using Neural Networks: Experimental Approach for N-1-1 Contingency

2016 ◽  
Vol 17 (6) ◽  
pp. 703-716 ◽  
Author(s):  
Sina Zarrabian ◽  
Rabie Belkacemi ◽  
Adeniyi A. Babalola
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Smart Grids ◽  
Intelligent Systems ◽  
Large Scale ◽  
Frequency Control ◽  
Test System ◽  
Selection Strategy ◽  
Control Approach ◽  
Contingency Condition

Abstract In this paper, a novel intelligent control is proposed based on Artificial Neural Networks (ANN) to mitigate cascading failure (CF) and prevent blackout in smart grid systems after N-1-1 contingency condition in real-time. The fundamental contribution of this research is to deploy the machine learning concept for preventing blackout at early stages of its occurrence and to make smart grids more resilient, reliable, and robust. The proposed method provides the best action selection strategy for adaptive adjustment of generators’ output power through frequency control. This method is able to relieve congestion of transmission lines and prevent consecutive transmission line outage after N-1-1 contingency condition. The proposed ANN-based control approach is tested on an experimental 100 kW test system developed by the authors to test intelligent systems. Additionally, the proposed approach is validated on the large-scale IEEE 118-bus power system by simulation studies. Experimental results show that the ANN approach is very promising and provides accurate and robust control by preventing blackout. The technique is compared to a heuristic multi-agent system (MAS) approach based on communication interchanges. The ANN approach showed more accurate and robust response than the MAS algorithm.

scholarly journals Fast battery capacity estimation using convolutional neural networks

2020 ◽  
pp. 014233122096642 ◽  
Author(s):  
Yihuan Li ◽  
Kang Li ◽  
Xuan Liu ◽  
Li Zhang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Smart Grids ◽  
Estimation Method ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Capacity Estimation ◽  
Starting Point ◽  
Battery Capacity ◽  
Mean Square Errors

Lithium-ion batteries have been widely used in electric vehicles, smart grids and many other applications as energy storage devices, for which the aging assessment is crucial to guarantee their safe and reliable operation. The battery capacity is a popular indicator for assessing the battery aging, however, its accurate estimation is challenging due to a range of time-varying situation-dependent internal and external factors. Traditional simplified models and machine learning tools are difficult to capture these characteristics. As a class of deep neural networks, the convolutional neural network (CNN) is powerful to capture hidden information from a huge amount of input data, making it an ideal tool for battery capacity estimation. This paper proposes a CNN-based battery capacity estimation method, which can accurately estimate the battery capacity using limited available measurements, without resorting to other offline information. Further, the proposed method only requires partial charging segment of voltage, current and temperature curves, making it possible to achieve fast online health monitoring. The partial charging curves have a fixed length of 225 consecutive points and a flexible starting point, thereby short-term charging data of the battery charged from any initial state-of-charge can be used to produce accurate capacity estimation. To employ CNN for capacity estimation using partial charging curves is however not trivial, this paper presents a comprehensive approach covering time series-to-image transformation, data segmentation, and CNN configuration. The CNN-based method is applied to two battery degradation datasets and achieves root mean square errors (RMSEs) of less than 0.0279 Ah (2.54%) and 0.0217 Ah (2.93% ), respectively, outperforming existing machine learning methods.

Probabilistic Power Transformer Condition Monitoring in Smart Grids

2019 ◽  
Author(s):  
Jose I. Aizpurua ◽  
Unai Garro ◽  
Enaut Muxika ◽  
Mikel Mendicute ◽  
Ian P. Gilbert ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Smart Grids ◽  
Power Transformer

Neural networks for online learning of non-stationary data streams: a review and application for smart grids flexibility improvement

2020 ◽  
Vol 53 (8) ◽  
pp. 6111-6154
Author(s):  
Zeineb Hammami ◽  
Moamar Sayed-Mouchaweh ◽  
Wiem Mouelhi ◽  
Lamjed Ben Said
Keyword(s):  
Neural Networks ◽  
Online Learning ◽  
Data Streams ◽  
Smart Grids

Discriminating between External Short Circuit and Internal Winding Fault in Power Transformer using RBF Neural Networks

2013 ◽  
Author(s):  
Jittiphong Klomjit ◽  
Atthapol Ngaopitakkul ◽  
Chaiyan Jettanasen ◽  
Chaichan Pothisarn ◽  
Surakit Thongsuk
Keyword(s):  
Neural Networks ◽  
Power Transformer ◽  
Short Circuit ◽  
Rbf Neural Networks
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