Research on line overload identification of power system based on improved neural network algorithm

2020 ◽  
Vol 32 (24) ◽  
Author(s):  
Lin Yang ◽  
Zhiming Luo ◽  
Wangqing Lin ◽  
Shaozi Li
Keyword(s):  
Neural Network ◽  
Power System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Line Overload

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

A Hybrid Neural Network Algorithm for On-Line State Inference That Accounts for Differences in Inoculum of Cephalosporium acremonium in Fed-Batch Fermentors

2001 ◽  
Vol 91-93 (1-9) ◽  
pp. 341-352 ◽  
Author(s):  
Rosineide G. Silva ◽  
Antonio J. G. Cruz ◽  
Carlos O. Hokka ◽  
Raquel L.C. Giordano ◽  
Roberto C. Giordano
Keyword(s):  
Neural Network ◽  
Cephalosporium Acremonium ◽  
Fed Batch ◽  
Hybrid Neural Network ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
State Inference

A New Approach to Machinery Monitoring and Diagnostics Using Self-Organizing Maps

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Trend Analysis ◽  
New Approach ◽  
Machine System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Monitoring And Diagnostics ◽  
Self Organizing

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

Application of adaptive Lyapunov-based UPFC supplementary controller by neural network algorithm in multi-machine power system

2009 ◽  
Vol 91 (4-5) ◽  
pp. 187-195 ◽  
Author(s):  
Rahmat Allah Hooshmand ◽  
Ghader Isazadeh
Keyword(s):  
Neural Network ◽  
Power System ◽  
Network Algorithm ◽  
Neural Network Algorithm

Optimal neural network algorithm for on-line string matching

1998 ◽  
Vol 28 (5) ◽  
pp. 737-739 ◽  
Author(s):  
Yiu-Wing Leung ◽  
Jiang-She Zhang ◽  
Zong-Ben Xu
Keyword(s):  
Neural Network ◽  
String Matching ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm
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