Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network

2010 ◽  
Vol 4 (3) ◽  
pp. 177-192 ◽  
Author(s):  
D. Evagorou ◽  
P.L. Lewin ◽  
V. Efthymiou ◽  
A. Kyprianou ◽  
G.E. Georghiou ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Partial Discharge ◽  
Wavelet Packet Transform

scholarly journals An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2701 ◽  
Author(s):  
Masoud Ahmadipour ◽  
Hashim Hizam ◽  
Mohammad Lutfi Othman ◽  
Mohd Amran Mohd Radzi
Keyword(s):  
Neural Network ◽  
Shannon Entropy ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Detection Technique ◽  
Islanding Detection ◽  
Energy Entropy ◽  
Cost Parameters ◽  
Logarithmic Energy

This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

Fault Diagnosis on Transmission System of Wind Turbines Based on Wavelet Packet Transform and RBF Neural Networks

2013 ◽  
Vol 373-375 ◽  
pp. 1102-1105 ◽  
Author(s):  
Xiao Yun Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Transmission System ◽  
Wavelet Packet Transform ◽  
Fault Classification

Wind turbine transmission system with abundant fault feature and variable types, the vibration signal was a carrier of fault features and it can reflect most of the fault information in the wind turbine transmission system. As there were a large number of transient and non-stationary signals accompany with the vibration signals, so wavelet packet transform was adopted for feature extraction. As RBF Neural network has a strong nonlinear mapping ability and self-adaptability, so it was introduced to the diagnosis system for network training, the neural networks structure and learning algorithm was presented, which could enhance the accuracy of diagnosis. The two-level neural networks recognition method was proposed, first level for fault classification and second level for fault diagnosis. The example shows that this method can be effectively applied to transmission system of wind turbine fault diagnosis with wavelet packet algorithm for fault feature extraction and RBF neural network for pattern recognition.

Rock hardness identification based on optimized PNN and multi-source data fusion

2021 ◽  
pp. 095440622110420
Author(s):  
Ying He ◽  
Muqin Tian ◽  
Jiancheng Song ◽  
Junling Feng
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Differential Evolution Algorithm ◽  
Vibration Signal ◽  
Optimization Method ◽  
Rock Wall ◽  
Rock Hardness ◽  
Source Data

To solve the problem that it is difficult to identify the cutting rock wall hardness of the roadheader in coal mine, a recognition method of cutting rock wall hardness is proposed based on multi-source data fusion and optimized probabilistic neural network. In this method, all kinds of cutting signals (the vibration signal of cutting arm, the pressure signal of hydraulic cylinders and current signal of cutting motor) are analyzed by wavelet packet to extract the feature vector, and the multi feature signal sample database of rock cutting with different hardness is established. To solve the problems of uncertain spread and complex network structure of probabilistic neural network (PNN), a PNN optimization method based on differential evolution algorithm (DE) and QR decomposition was proposed, and the rock hardness was identified based on multi-source data fusion by optimizing PNN. Then, based on the ground test monitoring data of a heavy longitudinal roadheader, the method is applied to recognize the cutting rock hardness, and compared with other common pattern recognition methods. The experimental results show that the cutting rock hardness recognition based on multi-source data fusion and optimized PNN has higher recognition accuracy, and the overall recognition error is reduced to 6.8%. The recognition of random cutting rock hardness is highly close to the actual. The method provides theoretical basis and technical premise for realizing automatic and intelligent cutting of heading face.

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