scholarly journals Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chun Yan ◽  
Meixuan Li ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Determination ◽  
Oil Analysis ◽  
Dissolved Gas ◽  
Adaboost Algorithm ◽  
Diagnosis Model ◽  
The Stability ◽  
Transformer Fault

Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.

New Methold on Power Transformer Fault Diagnosis

2012 ◽  
Vol 217-219 ◽  
pp. 2623-2628
Author(s):  
Nan Lan Wang ◽  
Ming Shan Cai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Wavelet Neural Network ◽  
Bp Algorithm ◽  
Oil Analysis ◽  
Wavelet Network ◽  
Dissolved Gas ◽  
Transformer Fault

This paper improves the simple genetic algorithm and combines genetic algorithm with BP algorithm to the wavelet neural network in the power transformer fault diagnosis by dissolved gas-in-oil analysis, Simulation result shows the problem was solved that wavelet network settles into local small extremum so easily that the network surging will increase and the network will not be convergent if the initialization is unreasonable, and overcomes the shortcoming that the speed is too slow if use genetic algorithm to train neural network independently.

Improved BP Neural Network in Diagnosis of Nonlinear Fault

2013 ◽  
Vol 401-403 ◽  
pp. 1055-1058
Author(s):  
Bin Xu ◽  
Xiao Ju Shen ◽  
Wei Ning Xue
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Newton Method ◽  
Bp Neural Network ◽  
Gas Content ◽  
Simulation Experiments ◽  
Nonlinear Characteristics ◽  
Content Ratio ◽  
Diagnosis Model ◽  
Transformer Fault

According to the nonlinear characteristics of transformer fault symptoms and fault types, the application of BP neural network to the problem of transformer fault diagnosis is presented. With a characteristic of the gas content ratio as the input, fault diagnosis model is established by using MATLAB software to achieve improved Newton method. And the simulation experiments show the effectiveness of the model of fault diagnosis.

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

scholarly journals Technical and Tactical Diagnosis Model of Table Tennis Matches Based on BP Neural Network

2021 ◽  
Author(s):  
Wenwen Huang ◽  
Miaomiao Lu ◽  
Yuxuan Zeng ◽  
Mengyue Hu ◽  
Yi Xiao
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Diagnostic Model ◽  
Table Tennis ◽  
Winning Probability ◽  
Highly Efficient ◽  
Diagnosis Model

Abstract Background: The technical and tactical diagnosis of table tennis is extremely important for the preparation of matches, and there is a nonlinear relationship between athletes’ performance and their sports quality. As the neural network model has high nonlinear dynamic processing ability and has high fitting accuracy, the main purpose of this study was to establish a technical and tactical diagnosis model of table tennis matches based on a neural network to diagnose the influence of athletes’ techniques and tactics on the competition result. Methods: A three-layer back propagation neural network model for table tennis match diagnosis were established. The 30 technical and tactical analysis indexes that are closely related to winning a competition were selected based on the double three-phase evaluation method. And 100 table tennis matches were selected as data sample, of which 70 matches were taken as training sample to establish the diagnostic model, the other 30 matches were used to test the validity of the diagnostic model.Results: The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high precision up to 99.997% and highly efficient in fitting (R2 = 0.99). It had a good ability to diagnose the technical and tactical abilities of table tennis players. The technical and tactical diagnosis results showed that the scoring rate of the fourth stroke of Harimoto had the greatest influence on the winning probability.Conclusion: The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high precision and highly efficient in fitting. By using this model, the weights of the influence of athletes’ technical and tactical indexes on the winning probability of the competition can be calculated, which provides a valuable reference for formulating targeted training plans for players.

scholarly journals Research on Financial Early-warning based on GIHS Improved BP_AdaBoost Algorithm

2018 ◽  
Vol 173 ◽  
pp. 03004
Author(s):  
Gui-fang Shen ◽  
Yi-Wen Zhang
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Early Warning ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Adaboost Algorithm ◽  
Good Point ◽  
Ensemble Learning Algorithm ◽  
Point Set ◽  
Good Point Set

To improve the accuracy of the financial early warning of the company, aiming at defects of slow learning speed, trapped in local solution and inaccurate operating result of the traditional BP neural network with random initial weights and thresholds, a parallel ensemble learning algorithm based on improved harmony search algorithm using good point set (GIHS) optimize the BP_Adaboost is proposed. Firstly, the good-point set is used to construct a more high quality initial harmony library, and it adjusts the parameters dynamically during the search process and generates several solutions in each iteration so as to make full use of information of harmony memory to improve the global search ability and convergence speed of algorithm. Secondly, ten financial indicators are chosen as the inputs of BP neural network value, and GIHS algorithm and BP neural network are combined to construct the parallel ensemble learning algorithm to optimize BP neural network initial weights value and output threshold value. Finally, many of these weak classifier is composed as strong classifier through the AdaBoost algorithm. The improved algorithm is validated in the company's financial early warning. Simulation results show that the performance of GIHS algorithm is better than the basic HS and IHS algorithm, and the GIHS-BP_AdaBoost classifier has higher classification and prediction accuracy.

scholarly journals Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Heng Ren ◽  
Yongjian Zhu ◽  
Ping Wang ◽  
Peng Li ◽  
Yuqun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Investigation ◽  
Recognition System ◽  
Quantitative Prediction ◽  
Widespread Application ◽  
Coal Roadway ◽  
Roof Stability ◽  
Stability Classification ◽  
The Stability

In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

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