scholarly journals Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Manoj Tripathy
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Power Transformer ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Optimal Number ◽  
Differential Protection ◽  
Harmonic Restraint ◽  
Transformer Differential Protection

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.

Detection of Citrus HuangLongBing Based on Principal Component Analysis and Back Propagation Neural Network

2014 ◽  
Vol 43 (4) ◽  
pp. 430002 ◽  
Author(s):  
邓小玲 DENG Xiao-ling ◽  
孔晨 KONG Chen ◽  
吴伟斌 WU Wei-bin ◽  
梅慧兰 MEI Hui-lan ◽  
李震 LI Zhen ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Citrus Huanglongbing

Performance Comparison between ANN and PCA Techniques for Road Signs Recognition

2013 ◽  
Vol 393 ◽  
pp. 611-616 ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Yasir Mohd Mustafah ◽  
Nahrul Khair Alang Md Rashid
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Performance Comparison ◽  
Feed Forward Neural Network ◽  
Classification Rate ◽  
The Road ◽  
Road Signs

This study reports about a comparison in recognizing road signs between Neural Network and Principal Component Analysis (PCA). The road sign with circular, triangular, octagonal and diamond shapes have been used in this study. Two recognition systems to determine the classes of the road signs class were implemented which are based on Feed Forward Neural Network and Principal Component Analysis (PCA). The performance of the trained classifier using Scaled Conjugate Gradient (SCG) back propagation function in Neural Network and PCA technique were evaluated on our test datasets. The experiments show that the system using PCA has a higher accuracy as compared to Neural Network with a minimum of 94% classification rate of road signs.

scholarly journals Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1379 ◽  
Author(s):  
Zhuang Yang ◽  
Qu Zhou ◽  
Xiaodong Wu ◽  
Zhongyong Zhao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Water Content ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Transformer Oil ◽  
Bpnn Model

The water content in oil is closely related to the deterioration performance of an insulation system, and accurate prediction of water content in oil is important for the stability and security level of power systems. A novel method of measuring water content in transformer oil using multi frequency ultrasonic with a back propagation neural network that was optimized by principal component analysis and genetic algorithm (PCA-GA-BPNN), is reported in this paper. 160 oil samples of different water content were investigated using the multi frequency ultrasonic detection technology. Then the multi frequency ultrasonic data were preprocessed using principal component analysis (PCA), which was implemented to obtain main principal components containing 95% of original information. After that, a genetic algorithm (GA) was incorporated to optimize the parameters for a back propagation neural network (BPNN), including the weight and threshold. Finally, the BPNN model with the optimized parameters was trained with a random 150 sets of pretreatment data, and the generalization ability of the model was tested with the remaining 10 sets. The mean squared error of the test sets was 8.65 × 10−5, with a correlation coefficient of 0.98. Results show that the developed PCA-GA-BPNN model is robust and enables accurate prediction of a water content in transformer oil using multi frequency ultrasonic technology.

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