Reliability Test Method of Power Grid Security Control System Based on BP Neural Network and Dynamic Group Simulation

2021 ◽  
Author(s):  
Qibing Wang ◽  
Xin Du ◽  
Kai Zhang ◽  
Junjun Pan ◽  
Weiguo Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Power Grid ◽  
Test Method ◽  
Reliability Test ◽  
Grid Security ◽  
Dynamic Group ◽  
Security Control

Fault Identification of Power Grid Based on BP Neural Network

2021 ◽  
pp. 361-367
Author(s):  
Mingjiu Pan ◽  
Zhou Lan ◽  
Kai Yang ◽  
Zhifang Yu ◽  
Huaiyue Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Power Grid ◽  
Fault Identification ◽  
Grid Based

Evaluating Suppliers of Chinese Power Grid Companies Based on BP Neural Network

2013 ◽  
Vol 8 (6) ◽  
pp. 81-90
Author(s):  
Yanfu Zhang ◽  
Qian Wang ◽  
Nan Shen ◽  
Hongqing Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Power Grid

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

Paste-filling weighing control system optimization based on neural network

2017 ◽  
Vol 14 (2) ◽  
pp. 155-158 ◽  
Author(s):  
Guimei Wang ◽  
Yong Shuo Zhang ◽  
Lijie Yang ◽  
Shuai Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Back Propagation ◽  
System Optimization ◽  
Filling Material ◽  
Content Type ◽  
Control Precision ◽  
Filling System

Purpose This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system. Design/methodology/approach The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error. Findings Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes. Originality/value The method can be further used to improve the control precision of the coal mine paste-filling system.

Smith Predictor Based on Predicting Induced-Delay

2011 ◽  
Vol 201-203 ◽  
pp. 2003-2006
Author(s):  
Shu De Li ◽  
Yi Chen ◽  
Cai Xia Liu
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Control System ◽  
Bp Neural Network ◽  
Networked Control System ◽  
Smith Predictor ◽  
Networked Control ◽  
Object Model ◽  
Random Delay

Since communication network is introduced into control system, induced-delay appears. Because of the delay, the performance of networked control system becomes bad, even unsteady. Conventional Smith predictor is sensitive to error in object model and needs delay’s value in advance. Regarding random delay, its application is limited. In this paper, we propose a method based on induced-delay predicted by BP neural network, which use two historical delay values to predict the next one. Smith predictor adjusts its parameters according to that value in time. The simulating results indicate that the precision of delay-predicting can be ensured and the performance of networked control system has been improved.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

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