Low Voltage Risk Assessment in Power System Using Neural Network Ensemble

2006 ◽  
pp. 1416-1421 ◽  
Author(s):  
Wei-Hua Chen ◽  
Quan-Yuan Jiang ◽  
Yi-Jia Cao
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Power System ◽  
Low Voltage ◽  
Neural Network Ensemble

scholarly journals Online Operation Risk Assessment of the Wind Power System of the Convolution Neural Network (CNN) Considering Multiple Random Factors

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 464
Author(s):  
Qingwu Gong ◽  
Si Tan ◽  
Yubo Wang ◽  
Dong Liu ◽  
Hui Qiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Power System ◽  
Wind Power ◽  
Convolution Neural Network ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Online Application ◽  
Wind Power System ◽  
Operation Risk

In order to solve the problem of the inaccuracy of the traditional online operation risk assessment model based on a physical mechanism and the inability to adapt to the actual operation of massive online operation monitoring data, this paper proposes an online operation risk assessment of the wind power system of the convolution neural network (CNN) considering multiple random factors. This paper analyzes multiple random factors of the wind power system, including uncertain wind power output, load fluctuations, frequent changes in operation patterns, and the electrical equipment failure rate, and generates the sample data based on multi-random factors. It uses the CNN algorithm network, offline training to obtain the risk assessment model, and online application to obtain the real-time online operation risk state of the wind power system. Finally, the online operation risk assessment model is verified by simulation using the standard network of 39 nodes of 10 machines New England system. The results prove that the risk assessment model presented in this paper is more rapid and suitable for online application.

Electric Power System Low Voltage Risk Assessment Based on the Utility Theory and Node Importance

2013 ◽  
Vol 291-294 ◽  
pp. 2278-2282
Author(s):  
Zong Qi Liu ◽  
Meng Si Tan ◽  
Jie Ji ◽  
Yong Tao Shen ◽  
Jian Hua Zhang
Keyword(s):  
Risk Assessment ◽  
Power System ◽  
Utility Theory ◽  
Low Voltage ◽  
Electric Power System ◽  
Assessment Model ◽  
Voltage Security ◽  
Security Issues ◽  
Node Importance ◽  
Set Up

To deal with the power system static voltage security issues, this paper provides a power system low voltage risk assessment model. We constructed a low voltage consequence severity model by combining the utility function and node importance, and set up a low voltage evaluation index based on risk theory. The validity and reasonability of the model are proved by the IEEE14 bus system.

Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems

2020 ◽  
Vol 27 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Aleksandar Radonjić ◽  
Danijela Pjevčević ◽  
Vladislav Maraš
Keyword(s):  
Neural Network ◽  
Decision Support Tool ◽  
Primary Objective ◽  
Danube River ◽  
Decision Making Process ◽  
Neural Network Ensemble ◽  
Support Tool ◽  
Shipping Company ◽  
The Republic ◽  
Power Measurements

AbstractThis paper investigates the use of neural networks (NNs) for the problem of assigning push boats to barge convoys in inland waterway transportation (IWT). Push boat–barge convoy assignmentsare part of the daily decision-making process done by dispatchers in IWT companiesforwhich a decision support tool does not exist. The aim of this paper is to develop a Neural Network Ensemble (NNE) model that will be able to assist in push boat–barge convoy assignments based on the push boat power.The primary objective of this paper is to derive an NNE model for calculation of push boat Shaft Powers (SHPs) by using less than 100% of the experimental data available. The NNE model is applied to a real-world case of more than one shipping company from the Republic of Serbia, which is encountered on the Danube River. The solution obtained from the NNE model is compared toreal-world full-scale speed/power measurements carried out on Serbian push boats, as well as with the results obtained from the previous NNE model. It is found that the model is highly accurate, with scope for further improvements.

Sign in / Sign up

Export Citation Format

Share Document