scholarly journals Analysis on the Stability of Reservoir Soil Slope Based on Fuzzy Artificial Neural Network

2013 ◽  
Vol 5 (2) ◽  
pp. 465-469 ◽  
Author(s):  
Lianguang Mo ◽  
Zheng Xie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Slope ◽  
Artificial Neural ◽  
The Stability

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

scholarly journals The stability analysis of dam walls using artificial neural network

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rock Slopes ◽  
Factors Affecting ◽  
Network Algorithm ◽  
Before And After ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
The Stability ◽  
Artificial Neural Network Algorithm

The stability of rock slopes of the walls of Roodbar dam in Lorestan is investigated using multi-layer Perceptron of artificial neural network algorithm. Then, the stability of rock slopes is studied by considered factors affecting stability at before and after impounding dam. The calculation is done on the factors affecting stability using artificial neural network algorithm. Finally, the results show that rock slopes of the walls of Roodbar dam in Lorestan in a dry state are stable at seventeen modes and unstable at three modes. Also, in a saturated state are stable at fourteen modes and unstable at six modes, furthermore have generally a little stability. The results of this paper indicated that the calculation are augmentable with experimental results.

BP Artificial Neural Network Study on Slop Stability

2012 ◽  
Vol 170-173 ◽  
pp. 1243-1246
Author(s):  
Bao Jian Zhang ◽  
Guang Qing Yang ◽  
Bao Lin Xiong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Stability Prediction ◽  
Bp Network ◽  
Forecasting Accuracy ◽  
Bp Artificial Neural Network ◽  
Intrinsic Factors ◽  
Artificial Neural ◽  
The Stability

Based on the introduction of Artificial Neural Network principle and analyzing steps, a neural network for slop stability prediction is built in this paper. Intrinsic factors and external factors of slop stability are considered in the network, through building, training and testing the BP network model, we can see that the BP network model can analyze and determine the stability of slop; the forecasting accuracy is high and we can use it as the decision basis of slop stability analysis.

scholarly journals Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

2019 ◽  
Vol 54 ◽  
Author(s):  
Carlos Alberto Araújo Júnior ◽  
Pábulo Diogo de Souza ◽  
Adriana Leandra de Assis ◽  
Christian Dias Cabacinha ◽  
Helio Garcia Leite ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Quantile Regression ◽  
Site Index ◽  
Permanent Plots ◽  
Artificial Neural ◽  
The Stability

Abstract: The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also better than the one obtained by the other methods, regardless of the presence or the absence of outliers in the database. This shows that the artificial neural network is a solid modelling technique in the presence of outliers. When the cause of the presence of outliers in the database is not known, they can be kept in it if techniques as artificial neural networks or quantile regression are used.

Study and Analysis of Power System Stabiliser Performance Using Artificial Neural Network

2014 ◽  
Vol 575 ◽  
pp. 605-609
Author(s):  
Muawia A. Magzoub ◽  
Nordin B. Saad ◽  
Rosdiazli B. Ibrahim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Dynamic Performance ◽  
Power System Stabilizer ◽  
Small Signal ◽  
Primary Method ◽  
Artificial Neural ◽  
The Stability ◽  
System Stabilizer

This paper represents a study and analysis of designing a PSS (Power system stabilizer) to be used with SMIB (a Single Machine and Infinite Bus) system that was developed using ANN (Artificial Neural Network). Furthermore, the dynamic performance of ANN based stabilizer is established and compared with conventional types of PSS. The proposed scheme’s effectiveness was tested through simulation in order to analyse the stability features of the small signal of the system regarding the operating situation of the steady state when a transmission line is lost. The focus of the primary method was on how the control performed. This was later confirmed to possess the level of a reaching time that was shorter and a spike that was lower.

scholarly journals Stability prediction of Himalayan residual soil slope using artificial neural network

2020 ◽  
Vol 103 (3) ◽  
pp. 3523-3540 ◽  
Author(s):  
Arunava Ray ◽  
Vikash Kumar ◽  
Amit Kumar ◽  
Rajesh Rai ◽  
Manoj Khandelwal ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Residual Soil ◽  
Stability Prediction ◽  
Soil Slope ◽  
Artificial Neural ◽  
Residual Soil Slope
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