Fuzzified time-frequency method for identification and localization of power system faults

2021 ◽  
pp. 1-13
Author(s):  
Pullabhatla Srikanth ◽  
Chiranjib Koley
Keyword(s):  
Power System ◽  
High Accuracy ◽  
Fuzzy Decision ◽  
Frequency Method ◽  
Time Frequency ◽  
Novel Approach ◽  
S Transform ◽  
Different Types ◽  
Power System Faults ◽  
Frequency Magnitude

In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM.

scholarly journals Inverse S-Transform Based Decision Tree for Power System Faults Identification

2011 ◽  
Vol 9 (1) ◽  
pp. 99 ◽  
Author(s):  
Srikanth Pullabhatla ◽  
Ashwani Kumar Chandel ◽  
Anil Naik Kanasottu
Keyword(s):  
Decision Tree ◽  
Power System ◽  
S Transform ◽  
Power System Faults

Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network

2019 ◽  
Vol 30 (04) ◽  
pp. 1950024 ◽  
Author(s):  
Guoyang Liu ◽  
Weidong Zhou ◽  
Minxing Geng
Keyword(s):  
Proper Time ◽  
Roc Curves ◽  
Seizure Detection ◽  
Detection Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Time Frequency ◽  
Novel Approach ◽  
S Transform ◽  
Stockwell Transform ◽  
Event Based

Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work, a novel approach, combining Stockwell transform (S-transform) with deep Convolutional Neural Networks (CNN), is proposed to detect seizure onsets in long-term intracranial EEG recordings. Primarily, raw EEG data is filtered with wavelet decomposition. Then, S-transform is used to obtain a proper time-frequency representation of each EEG segment. After that, a 15-layer deep CNN using dropout and batch normalization serves as a robust feature extractor and classifier. Finally, smoothing and collar technique are applied to the outputs of CNN to improve the detection accuracy and reduce the false detection rate (FDR). The segment-based and event-based evaluation assessments and receiver operating characteristic (ROC) curves are employed for the performance evaluation on a public EEG database containing 21 patients. A segment-based sensitivity of 97.01% and a specificity of 98.12% are yielded. For the event-based assessment, this method achieves a sensitivity of 95.45% with an FDR of 0.36/h.

Power System Events Classification using Pattern Recognition Approach

2006 ◽  
Vol 6 (1) ◽  
Author(s):  
S. R. Samantaray ◽  
P. K. Dash ◽  
G. Panda
Keyword(s):  
Pattern Recognition ◽  
Power System ◽  
Accurate Method ◽  
Frequency Resolution ◽  
New Approach ◽  
Time Frequency ◽  
Absolute Phase ◽  
S Transform ◽  
S Matrix ◽  
Stationary Signals

A new approach for power system event recognition and classification using HS-transform and RBFNN is presented in this paper. Different power system events (disturbances) like sag, swell, notch, spike, transient, and chirp are generated and processed through Hyperbolic S-transform (HS-Transform). The excellent time-frequency resolution property of HS-Transform is used to extract useful information (features) from the non-stationary signals for pattern recognition. Here HS-transform generates the S-matrix and S-matrix provides the time-frequency contours, phase contours and absolute phase of the corresponding signal. From the above extracted information, various numerical indices like standard deviation, variance, norm, energy are found out. Further these indices are used as inputs to the Radial Basis Function Neural Network (RBFNN) for classifying different power system events accordingly. The RBFNN provides accurate results even with inputs (indices) found out under high noise conditions (SNR 20 dB). Thus the proposed method provides a robust and accurate method for power system events classification.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

scholarly journals A Novel Ultrasonic TOF Ranging System Using AlN Based PMUTs

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 284
Author(s):  
Yihsiang Chiu ◽  
Chen Wang ◽  
Dan Gong ◽  
Nan Li ◽  
Shenglin Ma ◽  
...  
Keyword(s):  
Clock Cycle ◽  
High Accuracy ◽  
Ultrasonic Waves ◽  
Oxide Semiconductor ◽  
Average Error ◽  
Cmos Process ◽  
Clock Frequency ◽  
Time Frequency ◽  
Range Finding ◽  
Wide Range

This paper presents a high-accuracy complementary metal oxide semiconductor (CMOS) driven ultrasonic ranging system based on air coupled aluminum nitride (AlN) based piezoelectric micromachined ultrasonic transducers (PMUTs) using time of flight (TOF). The mode shape and the time-frequency characteristics of PMUTs are simulated and analyzed. Two pieces of PMUTs with a frequency of 97 kHz and 96 kHz are applied. One is used to transmit and the other is used to receive ultrasonic waves. The Time to Digital Converter circuit (TDC), correlating the clock frequency with sound velocity, is utilized for range finding via TOF calculated from the system clock cycle. An application specific integrated circuit (ASIC) chip is designed and fabricated on a 0.18 μm CMOS process to acquire data from the PMUT. Compared to state of the art, the developed ranging system features a wide range and high accuracy, which allows to measure the range of 50 cm with an average error of 0.63 mm. AlN based PMUT is a promising candidate for an integrated portable ranging system.

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