On-Line Detection Method of Transformer Measurement Error Based On BP Neural Network

2019 ◽  
Author(s):  
Helong Li ◽  
Jia Liu ◽  
Xiaolei Yuan ◽  
Xiaojian Zhao ◽  
Jinquan Zhao
Keyword(s):  
Neural Network ◽  
Measurement Error ◽  
Bp Neural Network ◽  
Detection Method ◽  
Line Detection ◽  
On Line

Research Situation Analysis of On-line Detection Technology for Power Transformer Winding Deformation

2020 ◽  
Vol 13 (6) ◽  
pp. 823-832
Author(s):  
Zhenhua Li ◽  
Weihui Jiang ◽  
Li Qiu ◽  
Zhenxing Li ◽  
Yanchun Xu
Keyword(s):  
Detection Method ◽  
Theoretical Research ◽  
Line Detection ◽  
Response Analysis ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
The Future ◽  
Transformer Winding ◽  
On Line ◽  
Winding Deformation

Background: Winding deformation is one of the most common faults in power transformers, which seriously threatens the safe operation of transformers. In order to discover the hidden trouble of transformer in time, it is of great significance to actively carry out the research of transformer winding deformation detection technology. Methods: In this paper, several methods of winding deformation detection with on-line detection prospects are summarized. The principles and characteristics of each method are analyzed, and the advantages and disadvantages of each method as well as the future research directions are expounded. Finally, aiming at the existing problems, the development direction of detection method for winding deformation in the future is prospected. Results: The on-line frequency response analysis method is still immature, and the vibration detection method is still in the theoretical research stage. Conclusion: The ΔV − I1 locus method provides a new direction for on-line detection of transformer winding deformation faults, which has certain application prospects and practical engineering value.

On-Line Calculation Method of Exhaust Steam Humidity Based on BP Neural Network for Steam Turbine

2013 ◽  
Author(s):  
Likun Zheng ◽  
Chang Chen ◽  
Danmei Xie ◽  
Hengliang Zhang ◽  
Yanzhi Yu
Keyword(s):  
Neural Network ◽  
Steam Turbine ◽  
Bp Neural Network ◽  
Calculation Method ◽  
Thermal Power ◽  
Engineering Application ◽  
Measuring Method ◽  
Factors Affecting ◽  
On Line ◽  
Turbine Exhaust

For condensing turbine, steam exhaust point is in wet steam area. The exhaust steam humidity of steam turbine is difficult to get due to lacking of effective measuring method. Calculation of exhaust steam humidity has always been one of the key parts of the analysis of thermal power units. The main factors affecting exhaust steam humidity are turbine load and turbine exhaust pressure etc, and they are of non-linearity. This paper develops a calculation method to calculate exhaust steam humidity based on BP neural network. Taking a N1000-25/600/600 ultra-supercritical (USC) steam turbine as an example, the exhaust steam humidity is calculated and the results show that the method has a good accuracy to meet the needs of the engineering application.

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.

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