scholarly journals Efficient fault identification scheme of compensated transmission grid based on correlated reactive power measurements and discrete wavelet transform

2021 ◽  
Vol 72 (4) ◽  
pp. 217-228
Author(s):  
Mohamed I. Zaki ◽  
Ragab A. El-Sehiemy ◽  
Ghada M. Amer
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Reactive Power ◽  
Short Circuit ◽  
Measuring System ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
Identification Scheme ◽  
Transmission Grid ◽  
Wide Range

Abstract The early fault identification in high-voltage power systems is a substantial aspect not only to minimize equipment failure but also to increase both the reliability and stability in power system. Subsequently, the aim of this paper is to propose the adaptive fault-identification scheme based on multi-resolution analysis technique. The proposed method is dependent on monitoring both voltages and currents from single-ended measuring system. The correlation among the reactive power computation and discrete wavelet transform is used to generate the significant criteria which are used to discriminate between short-circuit currents and energizing heavy loads behaviour. Different transmission network configurations are investigated to assess the dependability, security, and reliability of fault identification relay as well. The correlative protection scheme attains the accurate results under healthy disturbances, and therefore it is superior to other conventional approaches. In addition, a selective study is applied to different mother wavelets to find the best one. The response of the proposed scheme to the compensated transmission line is also verified at a wide range of compensation levels with faults before and after compensated bank. Simulation tests have been handled via ATP-EMTP to investigate the proper practicability and adaptability of the fault-identication relay.

scholarly journals Wavelet Transform Based Fault Identification and Reconfiguration for a Reduced Switch Multilevel Inverter Fed Induction Motor Drive

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1023
Author(s):  
Arigela Satya Veerendra ◽  
Akeel A. Shah ◽  
Mohd Rusllim Mohamed ◽  
Chavali Punya Sekhar ◽  
Puiki Leung
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Short Circuit ◽  
Open Circuit ◽  
Multilevel Inverter ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
The Novel ◽  
Inverter Topology

The multilevel inverter-based drive system is greatly affected by several faults occurring on switching elements. A faulty switch in the inverter can potentially lead to more losses, extensive downtime and reduced reliability. In this paper, a novel fault identification and reconfiguration process is proposed by using discrete wavelet transform and auxiliary switching cells. Here, the discrete wavelet transform exploits a multiresolution analysis with a feature extraction methodology for fault identification and subsequently for reconfiguration. For increasing the reliability, auxiliary switching cells are integrated to replace faulty cells in a proposed reduced-switch 5-level multilevel inverter topology. The novel reconfiguration scheme compensates open circuit and short circuit faults. The complexity of the proposed system is lower relative to existing methods. This proposed technique effectively identifies and classifies faults using the multiresolution analysis. Furthermore, the measured current and voltage values during fault reconfiguration are close to those under healthy conditions. The performance is verified using the MATLAB/Simulink platform and a hardware model.

scholarly journals Assessing the Contribution of the Oscillatory Potentials to the Genesis of the Photopic ERG with the Discrete Wavelet Transform

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Mathieu Gauvin ◽  
Allison L. Dorfman ◽  
Nataly Trang ◽  
Mercedes Gauthier ◽  
John M. Little ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Normal Subjects ◽  
Oscillatory Potentials ◽  
Discrete Wavelet ◽  
Wide Range ◽  
Frequency Components ◽  
Photopic Erg

The electroretinogram (ERG) is composed of slow (i.e., a-, b-waves) and fast (i.e., oscillatory potentials: OPs) components. OPs have been shown to be preferably affected in some diseases (such as diabetic retinopathy), while the a- and b-waves remain relatively intact. The purpose of this study was to determine the contribution of OPs to the building of the ERG and to examine whether a signal mostly composed of OPs could also exist. DWT analyses were performed on photopic ERGs (flash intensities: −2.23 to 2.64 log cd·s·m−2in 21 steps) obtained from normal subjects (n=40) and patients (n=21) affected with a retinopathy. In controls, the %OP value (i.e., OPs energy/ERG energy) is stimulus- and amplitude-independent (range: 56.6–61.6%; CV = 6.3%). In contrast, the %OPs measured from the ERGs of our patients varied significantly more (range: 35.4%–89.2%;p<0.05) depending on the pathology, some presenting with ERGs that are almost solely composed of OPs. In conclusion, patients may present with a wide range of %OP values. Findings herein also support the hypothesis that, in certain conditions, the photopic ERG can be mostly composed of high-frequency components.

Fault Detection and Classification in Smart Grids Using Wavelet Analysis

2020 ◽  
Author(s):  
Muhammad Ibrahim Munir ◽  
Sajid Hussain ◽  
Ali Al-Alili ◽  
Reem Al Ameri ◽  
Ehab El-Sadaany
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Smart Grids ◽  
Short Circuit ◽  
Low Frequency ◽  
Evaluation Study ◽  
Open Circuit ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Transmission Grid

Abstract One of the core features of the smart grid deemed essential for smooth grid operation is the detection and diagnosis of system failures. For a utility transmission grid system, these failures could manifest in the form of short circuit faults and open circuit faults. Due to the advent of the digital age, the traditional grid has also undergone a massive transition to digital equipment and modern sensors which are capable of generating large volumes of data. The challenge is to preprocess this data such that it can be utilized for the detection of transients and grid failures. This paper presents the incorporation of artificial intelligence techniques such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to detect and comprehensively classify the most common fault transients within a reasonable range of accuracy. For gauging the effectiveness of the proposed scheme, a thorough evaluation study is conducted on a modified IEEE-39 bus system. Bus voltage and line current measurements are taken for a range of fault scenarios which result in high-frequency transient signals. These signals are analyzed using continuous wavelet transform (CWT). The measured signals are afterward preprocessed using Discrete Wavelet Transform (DWT) employing Daubechies four (Db4) mother wavelet in order to decompose the high-frequency components of the faulty signals. DWT results in a range of high and low-frequency detail and approximate coefficients, from which a range of statistical features are extracted and used as inputs for training and testing the classification algorithms. The results demonstrate that the trained models can be successfully employed to detect and classify faults on the transmission system with acceptable accuracy.

A GENERALIZED MULTISCALE ANALYSIS OF THE PREDICTIVE CONTENT OF EURODOLLAR IMPLIED VOLATILITIES

2009 ◽  
Vol 12 (01) ◽  
pp. 1-18 ◽  
Author(s):  
ALESSANDRO CARDINALI
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiscale Analysis ◽  
Cross Correlation ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Predictive Information ◽  
Wide Range ◽  
Predictive Content ◽  
Periodic Components

It is widely believed that implied volatilities contains information that would enable prediction of spot volatility for a wide range of financial assets. Lead-lag analysis based on the Discrete Wavelet Transform has been proposed as one method for identifying and extracting that predictive information. Unfortunately this approach can fail to identify periodic components that are not proportional to an increasing dyadic scale. We propose a multiscale analysis of the Eurodollar realized volatility and at-the-money (ATM) implied volatilities. After filtering the long memory components we produce a decomposition of cross-correlation by using wavelet packet methods. A threshold cost functional based on asymptotic confidence intervals was used along with the best basis algorithm in order to select an adaptive frequency partition of the sample cross-correlation. We found substantial evidence that Eurodollar implied volatilities contain predictive information about realized volatilities. Moreover, in our analysis the new technique outperforms the lead-lag analysis based on the nondecimated Discrete Wavelet Transform. Therefore we contend that the proposed technique will improve detection of predictive information and recommend further testing in a range of applied contexts.

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