Real‐time recognition of power quality disturbance‐based deep belief network using embedded parallel computing platform

2020 ◽  
Vol 15 (4) ◽  
pp. 519-526
Author(s):  
Ziming Chen ◽  
Mengshi Li ◽  
Tianyao Ji ◽  
Qinghua Wu
Keyword(s):  
Parallel Computing ◽  
Real Time ◽  
Power Quality ◽  
Deep Belief Network ◽  
Belief Network ◽  
Computing Platform ◽  
Power Quality Disturbance

A real-time and ubiquitous network attack detection based on deep belief network and support vector machine

2020 ◽  
Vol 7 (3) ◽  
pp. 790-799 ◽  
Author(s):  
Hao Zhang ◽  
Yongdan Li ◽  
Zhihan Lv ◽  
Arun Kumar Sangaiah ◽  
Tao Huang
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Attack Detection ◽  
Deep Belief Network ◽  
Support Vector ◽  
Network Attack ◽  
Belief Network ◽  
Ubiquitous Network

scholarly journals Real-time classification and sensor fusion with a spiking deep belief network

2013 ◽  
Vol 7 ◽  
Author(s):  
Peter O'Connor ◽  
Daniel Neil ◽  
Shih-Chii Liu ◽  
Tobi Delbruck ◽  
Michael Pfeiffer
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Deep Belief Network ◽  
Belief Network ◽  
Real Time Classification

scholarly journals An advanced signal processing based multiclass power quality disturbance detection and classification technique for grid connected solar PV farm

2019 ◽  
Vol 8 (9) ◽  
pp. 876-896
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Real Time ◽  
Power Quality ◽  
Solar Pv ◽  
Target Feature ◽  
Classification Technique ◽  
Power Quality Disturbance ◽  
Processing Techniques ◽  
Disturbance Detection

This paper presents an efficient event detection and classification technique for multiple power quality (PQ) disturbances. Initially synthetic power quality disturbances are simulated and then are directly processed to proposed algorithms to generate the target feature sets which comprises of energy, entropy, root mean square (RMS), mean, standard deviation, kurtosis, variance and maximum peak respectively. After the overall data analysis, it was found that thirteen power quality events out of the overall generated PQ disturbances were distinctively classified. Eventually these target features are passed through simple decision tree based event classifier for PQ events classification. The proposed algorithms are change detection filter (CDFT) with noise, without noise and synchrosqueeze wavelet transform (SST) has been scrutinized for number of disturbances presented in the PQ events. The proposed technique SST is applied for PV based microgrid to enhance the real time performance of the proposed technique where it has been verified as a superior technique as compared with the some of the existing event classification techniques such as wavelet transform (WT), stock well transform (SR),etc. The entire process has been verified in the in the MATLAB /Editor. The proposed technique evades the need of further signal processing techniques for detection and classification PQ events, thus ensconced less computational complexity and faster execution. Hence it is an efficient algorithm for real time applications.

Sign in / Sign up

Export Citation Format

Share Document