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Статті в журналах з теми "SWITCHING WAVELET"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Türkmenoğlu, Veli, Mustafa Aktaş, Serkan Karataş, and Halil İbrahim Okumuş. "Soft Set-Based Switching Faults Decision Making in DTC Induction Motor Drives." Journal of Circuits, Systems and Computers 24, no. 02 (November 27, 2014): 1550021. http://dx.doi.org/10.1142/s0218126615500218.

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Анотація:

This paper introduces a method for detection and identification of IGBT-based drive open-circuit fault of DTC induction motor drives. The detection mechanism is based on soft set theory and wavelet decomposition, if it is detailed, ⊼-product decision making method and sym2 wavelet decomposition have been used in the detection mechanism. In this method, the stator currents have been used as an input to the system. The stator current has been used for the detection of the fault. The signal analysis has been performed up to the six level details wavelets decomposition. Faulty switch is detected by applying soft set theory to sixth level wavelets transformation. This is the first time applied to inverter in induction motor drives fault detection. The results demonstrate that the proposed fault detection and diagnosis system has very good capabilities.

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2

Guillén, Daniel, Gina Idárraga-Ospina, and Camilo Cortes. "A New Adaptive Mother Wavelet for Electromagnetic Transient Analysis." Journal of Electrical Engineering 67, no. 1 (January 1, 2016): 48–55. http://dx.doi.org/10.1515/jee-2016-0007.

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Abstract Wavelet Transform (WT) is a powerful technique of signal processing, its applications in power systems have been increasing to evaluate power system conditions, such as faults, switching transients, power quality issues, among others. Electromagnetic transients in power systems are due to changes in the network configuration, producing non-periodic signals, which have to be identified to avoid power outages in normal operation or transient conditions. In this paper a methodology to develop a new adaptive mother wavelet for electromagnetic transient analysis is proposed. Classification is carried out with an innovative technique based on adaptive wavelets, where filter bank coefficients will be adapted until a discriminant criterion is optimized. Then, its corresponding filter coefficients will be used to get the new mother wavelet, named wavelet ET, which allowed to identify and to distinguish the high frequency information produced by different electromagnetic transients.

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3

CHEN, WENJIE, XU YANG, and ZHAOAN WANG. "AN APPLICATION OF COMPLEX WAVELETS FOR HIGH FREQUENCY SWITCHING NOISE DETECTION." Journal of Circuits, Systems and Computers 18, no. 01 (February 2009): 97–102. http://dx.doi.org/10.1142/s0218126609004958.

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Switching signal is always composed of a series of fast voltage and current transient impulsive waves, which are created during turn-on and turn-off operation of the switching elements. This paper presents a scalogram and phase spectrum analyzing approach for the high-frequency switching noise detection. A complex Gaussian wavelet is used to extract the oscillation feature of the noise signal. The proposed scheme is more suitable in discriminating the type of switching noise than conventional wavelet transform approaches. Simulation results show the effectiveness and advantages of the method.

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4

FUKUMA, S. "Switching Wavelet Transform for ROI Image Coding." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 7 (July 1, 2005): 1995–2006. http://dx.doi.org/10.1093/ietfec/e88-a.7.1995.

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5

Alam, M. Shafiul, Md Shamimul Haque Chowdhury, and Muhammad Athar Uddin. "Power System Switching Transient Detection using Wavelet Transformed Based Signal Decomposition." IIUC Studies 7 (October 19, 2012): 241–48. http://dx.doi.org/10.3329/iiucs.v7i0.12270.

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Анотація:

Switching transient phenomena in Electric Power System develop several disturbances, sometimes very hazardous for the electrical equipment life, for the environment and for the human life. Switching transient phenomena produce over voltage, over current and electrical fields, which haven't to neglect. Several types of wavelet network algorithms have been considered for detection of power system switching transients. But both time and frequency information are accessible by multiresolution analysis (MRA). This paper presents a wavelet transform based multiresolution analysis of power system signal to detect, localize and extract switching transients. Power system switching transients have been simulated using MATLAB-7.01. The key idea underlying the approach is to decompose a distorted signal into other signals which represents a smoothed version and detailed version of the original signal. The decomposition is performed using multiresolution analysis. The proposed method appears to be robust for detection and localization of power quality disturbances produced due to load switching and capacitor switching. DOI: http://dx.doi.org/10.3329/iiucs.v7i0.12270 IIUC Studies Vol.7 2011: 241-248

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6

Gaeid, Khalaf S., Mshari Aead Asker, Nada N. Tawfeeq, and Salam Razooky Mahdi. "Computer Simulation of PMSM Motor with Five Phase Inverter Control using Signal Processing Techniques." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3697. http://dx.doi.org/10.11591/ijece.v8i5.pp3697-3710.

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Анотація:

The signal processing techniques and computer simulation play an important role in the fault diagnosis and tolerance of all types of machines in the first step of design. Permanent magnet synchronous motor (PMSM) and five phase inverter with sine wave pulse width modulation (SPWM) strategy is developed. The PMSM speed is controlled by vector control. In this work, a fault tolerant control (FTC) system in the PMSM using wavelet switching is introduced. The feature extraction property of wavelet analysis used the error as obtained by the wavelet de-noised signal as input to the mechanism unit to decide the healthy system. The diagnosis algorithm, which depends on both wavelet and vector control to generate PWM as current based manage any parameter variation. An open-end phase PMSM has a larger range of speed regulation than normal PMSM. Simulation results confirm the validity and effectiveness of the switching strategy.

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7

Lin, Chih-Min, Kun-Neng Hung, and Chun-Fei Hsu. "Adaptive Neuro-Wavelet Control for Switching Power Supplies." IEEE Transactions on Power Electronics 22, no. 1 (January 2007): 87–95. http://dx.doi.org/10.1109/tpel.2006.886630.

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8

Li, Hao, Meng Zhao, Hao Yan, and Xingwu Yang. "Nanoseconds Switching Time Monitoring of Insulated Gate Bipolar Transistor Module by Under-Sampling Reconstruction of High-Speed Switching Transitions Signal." Electronics 8, no. 10 (October 22, 2019): 1203. http://dx.doi.org/10.3390/electronics8101203.

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Анотація:

An insulated gate bipolar transistor (IGBT) is one of the most reliable critical components in power electronics systems (PESs). The switching time during IGBT turn-on/off transitions is a good health status indicator for IGBT. However, online monitoring of IGBT switching time is still difficult in practice due to the requirement of extremely high sampling rate for nanoseconds time resolution. The compressed sensing (CS) method shows a potential to overcome the technical difficult by reducing the sampling rate. To further improve the efficiency and reduce the computational time for IGBT online condition monitoring (CM), an under-sampling reconstruction method of an IGBT high-speed switching signal is presented in this paper. First, the physical mechanism and signal characteristics of IGBT switching transitions are analyzed. Then, by utilizing the sparse characteristics of IGBT switching signal in the wavelet domain, the wavelet basis is used for sparse representation. The stagewise orthogonal matching pursuit (StOMP) algorithm is proposed to enhance the convergence speed for switching signal reconstruction. Experiments are performed on not only a double-pulse test rig but also a real Pulse-Width Modulation (PWM) converter. Results show that the IGBT high-speed switching transitions signal can be accurately recovered with a reduced sampling rate and the nanoseconds switching time change can be monitored for IGBT CM.

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9

Ketabi, A., M. Khoshkholgh, and R. Feuillet. "A New Approach to Nonsinusoidal Steady-State Power System Analysis." Mathematical Problems in Engineering 2009 (2009): 1–18. http://dx.doi.org/10.1155/2009/584637.

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Анотація:

A new analysis method using wavelet domain for steady-state operating condition of power system is developed and introduced. Based on wavelet-Galerkin theory, the system components such as resistor, inductor, capacitor, transmission lines, and switching devices are modeled in discrete wavelet domain for the purpose of steady-state analysis. To solve system equations, they are transferred to wavelet domain by forming algebraic matrix-vector relations using the wavelet transform coefficients and the equivalent circuit is thus built for system simulation. After describing the new algorithm, two-case studies are presented and compared with the simulations in the time domain to verify the accuracy and computational performance.

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10

Patcharoen, Theerasak, Suntiti Yoomak, Atthapol Ngaopitakkul, and Chaichan Pothisarn. "Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence." Open Physics 16, no. 1 (April 18, 2018): 93–104. http://dx.doi.org/10.1515/phys-2018-0016.

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Abstract This paper describes the combination of discrete wavelet transforms (DWT) and artificial intelligence (AI), which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51) and unbalanced current (60C) protection relay for an application of shunt capacitor bank protection in the future.

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11

Sedighi, Alireza. "Classification of Transient Phenomena in Distribution System using wavelet Transform." Journal of Electrical Engineering 65, no. 3 (May 1, 2014): 144–50. http://dx.doi.org/10.2478/jee-2014-0022.

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Abstract An efficient procedure for classification of transient phenomena in distribution systems is proposed in this paper. The proposed method has been applied to classify some transient phenomena such as inrush current, load switching, capacitor switching and single phase to ground fault. The new scheme is based on wavelet transform algorithm. All of the events for feature extraction and test are simulated using Electro Magnetic Transient Program (EMTP). Results show high accuracy of proposed method.

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12

Chakib, Houda, Brahim Minaoui, Abderrahim Salhi, and Imad Badi. "Switching of Wavelet Transforms by Neural Network for Image Compression." Journal of Electronic Commerce in Organizations 16, no. 1 (January 2018): 43–56. http://dx.doi.org/10.4018/jeco.2018010104.

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Nowadays, digital images compression requires more and more significant attention of researchers. Even when high data rates are available, image compression is necessary in order to reduce the memory used, as well the transmission cost. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this article, a neural network is implemented for image compression using the feature of wavelet transform. The idea is that a back-propagation neural network can be trained to relate the image contents to its ideal compression method between two different wavelet transforms: orthogonal (Haar) and biorthogonal (bior4.4).

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13

Sinha, Pampa, Sudipta Debath, and Swapan Kumar Goswami. "Classification of Power Quality Events Using Wavelet Analysis and Probabilistic Neural Network." IAES International Journal of Artificial Intelligence (IJ-AI) 5, no. 1 (August 20, 2016): 1. http://dx.doi.org/10.11591/ijai.v5.i1.pp1-12.

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<p>Power quality studies have become an important issue due to widespread use of sensitive electronic equipment in power system. The sources of power quality degradation must be investigated in order to improve the power quality. Switching transients in power systems is a concern in studies of equipment insulation coordination. In this paper a wavelet based neural network has been implemented to classify the transients due to capacitor switching, motor switching, faults, converter and transformer switching. The detail reactive powers for these five transients are determined and a model which uses the detail reactive power as the input to the Probabilistic neural network (PNN) is set up to classify the above mentioned transients. The simulation has been executed for an 11kv distribution system. With the help of neural network classifier, the transient signals are effectively classified.</p>

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14

Veerendra, Arigela Satya, Akeel A. Shah, Mohd Rusllim Mohamed, Chavali Punya Sekhar, and Puiki Leung. "Wavelet Transform Based Fault Identification and Reconfiguration for a Reduced Switch Multilevel Inverter Fed Induction Motor Drive." Electronics 10, no. 9 (April 25, 2021): 1023. http://dx.doi.org/10.3390/electronics10091023.

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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.

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15

De, Bishnu Prasad, Rajib Kar, Durbadal Mandal, and Sakti Prasad Ghoshal. "Design of Optimal CMOS Inverter for Symmetric Switching Characteristics Using Firefly Algorithm with Wavelet Mutation." International Journal of Swarm Intelligence Research 5, no. 2 (April 2014): 29–64. http://dx.doi.org/10.4018/ijsir.2014040103.

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In this article, a population based meta-heuristic search method called Firefly Algorithm with Wavelet Mutation (FAWM) is applied for the optimal switching characterization of CMOS inverter. In Firefly Algorithm (FA), behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and brighter fireflies are considered as more attractive among the population. For the present minimization based optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. FAWM incorporates a new definition of swarm updating with the help of wavelet mutation based on wavelet theory. Wavelet mutation enhances the FA to explore the solution space more effectively compared with the other optimization methods. The performance of FAWM is compared with real coded genetic algorithm (RGA), and conventional PSO reported in the literature. FAWM based design results are also compared with the PSPICE results. The comparative simulation results establish the FAWM as a more competent optimization algorithm to other aforementioned evolutionary algorithms for the examples considered and can be efficiently used for CMOS inverter design.

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16

Szatmáry, Károly, and János Gál. "Wavelet Analysis of Some Pulsating Stars." International Astronomical Union Colloquium 137 (1993): 761–63. http://dx.doi.org/10.1017/s0252921100018832.

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The detection of a variable period is very important as it gives information on the evolutionary state or on the binary nature of a pulsating star. If a pulsator is moving in a binary system, its light curve is frequency-modulated by the orbital period (light-time effect). Mode switching or chaos also may be the cause of the changes in the amplitude and period of the light variations.The determination of period variability is difficult. Usually the conventional Fourier-spectrum cannot give any information about a possible period variation. The 0-C diagram is useful to detect a variable period, but it does not give the amplitude and phase values.

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17

Hou, Run-min, Yuan-long Hou, Chao Wang, Qiang Gao, and Hao Sun. "A Hybrid Wavelet Fuzzy Neural Network and Switching Particle Swarm Optimization Algorithm for AC Servo System." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9724917.

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A hybrid computational intelligent approach which combines wavelet fuzzy neural network (WFNN) with switching particle swarm optimization (SPSO) algorithm is proposed to control the nonlinearity, wide variation in loads, time variation, and uncertain disturbance of the high-power AC servo system. The WFNN method integrated wavelet transforms with fuzzy rules and is proposed to achieve precise positioning control of the AC servo system. As the WFNN controller, the back-propagation method is used for the online learning algorithm. Moreover, the SPSO is proposed to adapt the learning rates of the WFNN online, where the velocity updating equation is according to a Markov chain, which makes it easy to jump the local minimum, and acceleration coefficients are dependent on mode switching. Furthermore, the stability of the closed loop system is guaranteed by using the Lyapunov method. The results of the simulation and the prototype test prove that the proposed approach can improve the steady-state performance and possess strong robustness to both parameter perturbation and load disturbance.

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18

Yang, Chen, Li Zhu, Xiangpeng Tian, Yu Xiao, Qinghai Li, and Wei Liu. "Abrupt changes detection using wavelet-based denoising analysis." Journal of Physics: Conference Series 2283, no. 1 (June 1, 2022): 012009. http://dx.doi.org/10.1088/1742-6596/2283/1/012009.

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Abstract Switching power supply is an essential power supply method that exists in the development of the electronic information industry. Drive power supply has various quality problems, primarily due to the loss of power inductors; for power inductor capacity testing, making a feasibility assessment is of great significance. In this study, we present a wavelet-based approach for detecting sudden changes in the power inductor current curve. To reliably identify rapid changes in the saturation of the inductor current after energization, the discrete wavelet transform is utilized to analyze the signal within a specified subset of all scaling and translation values, displaying different local signal characteristics.

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19

Yang, Chen, Li Zhu, Xiangpeng Tian, Yu Xiao, Qinghai Li, and Wei Liu. "Abrupt changes detection using wavelet-based denoising analysis." Journal of Physics: Conference Series 2283, no. 1 (June 1, 2022): 012009. http://dx.doi.org/10.1088/1742-6596/2283/1/012009.

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Анотація:

Abstract Switching power supply is an essential power supply method that exists in the development of the electronic information industry. Drive power supply has various quality problems, primarily due to the loss of power inductors; for power inductor capacity testing, making a feasibility assessment is of great significance. In this study, we present a wavelet-based approach for detecting sudden changes in the power inductor current curve. To reliably identify rapid changes in the saturation of the inductor current after energization, the discrete wavelet transform is utilized to analyze the signal within a specified subset of all scaling and translation values, displaying different local signal characteristics.

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20

Huang, Yi-Xuan, and Kung-Ming Chung. "Mode Switching in a Compressible Rectangular Cavity Flow." Aerospace 10, no. 6 (May 26, 2023): 504. http://dx.doi.org/10.3390/aerospace10060504.

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This study determines the mean and fluctuating pressures for flow through a rectangular shallow cavity (ratio between the length and the depth = 2.43, 4.43, and 6.14; ratio between the length and the width = 0.5, 1.0, and 2.0) at a Mach number of 0.64 in a blowdown transonic wind tunnel. A amplitude modulation analysis is used for the post-processing of the fluctuating pressure signals. The spectral analysis (wavelet) shows the intermittent behavior of the discrete Rossiter–Heller modes. A correlation analysis determines that mode switching is more significant between the second and third modes (organized structures that are associated with shear layer vortices), particularly for two-dimensional and shallower cavities.

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21

Vatani, Mehrnoosh. "Transient Analysis of Switching the Distributed Generation Units in Distribution Networks." International Journal of Applied Power Engineering (IJAPE) 5, no. 3 (December 1, 2016): 130. http://dx.doi.org/10.11591/ijape.v5.i3.pp130-136.

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<p>Adding distributed Generators (DGs) to the passive electrical networks causes major changes in the specifications of the network including voltage profile, short circuit level and transient stability. In this paper, the effect of DGs switching transient in network is considered. The DGs location are changed in different buses. Two types of DGs are used (i.e. wind and synchronous DGs). Switching transient signals are time variant. It has a continuous spectrum of frequency. Fast Fourier and Wavelet transform methods are used for transient analysis. The proposed method is applied to IEEE-13 Bus distribution system.</p>

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22

Wang, Jie-Sheng, and Na-Na Shen. "Hybrid Multiple Soft-Sensor Models of Grinding Granularity Based on Cuckoo Searching Algorithm and Hysteresis Switching Strategy." Scientific Programming 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/146410.

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According to the characteristics of grinding process and accuracy requirements of technical indicators, a hybrid multiple soft-sensor modeling method of grinding granularity is proposed based on cuckoo searching (CS) algorithm and hysteresis switching (HS) strategy. Firstly, a mechanism soft-sensor model of grinding granularity is deduced based on the technique characteristics and a lot of experimental data of grinding process. Meanwhile, the BP neural network soft-sensor model and wavelet neural network (WNN) soft-sensor model are set up. Then, the hybrid multiple soft-sensor model based on the hysteresis switching strategy is realized. That is to say, the optimum model is selected as the current predictive model according to the switching performance index at each sampling instant. Finally the cuckoo searching algorithm is adopted to optimize the performance parameters of hysteresis switching strategy. Simulation results show that the proposed model has better generalization results and prediction precision, which can satisfy the real-time control requirements of grinding classification process.

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23

Hong, Y. Y., and C. W. Wang. "Switching Detection/Classification Using Discrete Wavelet Transform and Self-Organizing Mapping Network." IEEE Transactions on Power Delivery 20, no. 2 (April 2005): 1662–68. http://dx.doi.org/10.1109/tpwrd.2004.833921.

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24

Aktas, M., and V. Turkmenoglu. "Wavelet-based switching faults detection in direct torque control induction motor drives." IET Science, Measurement & Technology 4, no. 6 (November 1, 2010): 303–10. http://dx.doi.org/10.1049/iet-smt.2009.0121.

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25

Gonzalez, David, Jan T. Bialasiewicz, Josep Balcells, and Javier Gago. "Wavelet-Based Performance Evaluation of Power Converters Operating With Modulated Switching Frequency." IEEE Transactions on Industrial Electronics 55, no. 8 (August 2008): 3167–76. http://dx.doi.org/10.1109/tie.2008.921199.

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26

Dong, Hua Jun, Wen Liang Dong, and Dao Shun Wang. "Comparative Analysis on Noise Processing of Vacuum Switching Arc Images." Applied Mechanics and Materials 229-231 (November 2012): 2615–18. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.2615.

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Анотація:

In order to diagnose the arc shape and the plasma parameters of vacuum switching arc effectively, it is necessary to do digital image processing on the arc images. The noises interfere greatly in the digital image processing of the arc, it makes images fuzzy and submerges the characteristics of the images. In this paper, based on the methods of average filter, two dimensions wavelet filter, wiener filter, several images averaging filter and median filter are used to filter the salt and pepper noised arc images. Combined with the results, we have analyzed and compared the effects of different noises filtering methods.

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27

Kiss, L. L., Gy Szabó, K. Szatmáry, and J. A. Mattei. "Changes of the Physical State in Semiregular Variables." International Astronomical Union Colloquium 176 (2000): 117–18. http://dx.doi.org/10.1017/s0252921100057304.

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AbstractWe present a progress report on a detailed analysis of long-term (70–90 years) visual observations for seven semiregular variables. Fundamental changes of the physical state (amplitude and/or frequency modulations, mode change and switching) are studied with conventional Fourier- and wavelet analyses. Besides two examples of repetitive mode changes we report a simple geometric model of a rotationally induced amplitude modulation in RY Ursae Majoris.

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28

Mishra, Sanhita, Sarat Chandra Swain, and Ritesh Dash. "Switching transient analysis for low voltage distribution cable." Open Engineering 12, no. 1 (January 1, 2022): 29–37. http://dx.doi.org/10.1515/eng-2022-0004.

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Abstract Low voltage cable is primarily connected from the transmission system to several household applications. It is quite common that switching transient in the power system during the energization of the high voltage and low voltage cables have a very crippling effect on the cable as well as the power system components. Hence, an experiment has been performed in the laboratory with a low voltage cable-connected motor system. The experimental results have been validated in the simulation platform, and they are capable of predicting the transient behavior during power cable energization. The effect of transients on power cables during the energization of devices has been investigated in this study in the form of voltage, current, and frequency. Discrete wavelet transform is implemented for the decomposition of the transient current. The generated approximation signal is used to quantify the severity during switching transient condition.

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29

Singh, Varsha, S. Gupta, S. Pattnaik та Aarti Goyal. "A Novel Approach to GSA, GA and Wavelet Transform to Design Fuzzy Logic Controller for 1ϕ Multilevel Inverter". International Journal of Power Electronics and Drive Systems (IJPEDS) 7, № 4 (1 грудня 2016): 1200. http://dx.doi.org/10.11591/ijpeds.v7.i4.pp1200-1211.

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<p>This paper proposes a novel approach for obtaining a closed loop control scheme based on Fuzzy Logic Controller to regulate the output voltage waveform of multilevel inverter. Fuzzy Logic Controller is used to guide and control the inverter to synthesize a stepped output voltage waveform with reduced harmonics. In this paper, three different intelligent soft-computing methods are used to design a fuzzy system to be used as a closed loop control system for regulating the inverter output. Gravitational Search Algorithm and Genetic Algorithm are used as optimization methods to evaluate switching angles for different combination of input voltages applied to MLI. Wavelet Transform is used as synthesizing technique to shape stepped output waveform of inverter using orthogonal wavelet sets. The proposed FLC controlled method is carried out for a wider range of input dc voltages by considering ±10% variations in nominal voltage value. A 7-level inverter is used to validate the results of proposed control methods. The three proposed methods are then compared in terms of various parameters like computational time, switching angles and THD to justify the performance and system flexibility. Finally, hardware based results are also obtained to verify the viability of the proposed method.</p>

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Hari Kumar Raveendran Pillai, Mayadevi Nanappan, Mini Valiyakulam Prabhakaran, and Shenil Pushpangadan Sathyabhama. "A Robust Technique for Detection, Diagnosis, and Localization of Switching Faults in Electric Drives Using Discrete Wavelet Transform." International Journal of Engineering and Technology Innovation 13, no. 1 (January 1, 2023): 14–27. http://dx.doi.org/10.46604/ijeti.2023.10005.

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Detection, diagnosis, and localization of switching faults in electric drives are extremely important for operating a large number of induction motors in parallel. This study aims to present the design and development of switching fault detection, diagnosis, and localization strategy for the induction motor drive system (IMDS) by using a novel diagnostic variable that is derived from discrete wavelet transform (DWT) coefficients. The distinctiveness of the proposed algorithm is that it can identify single/multiple switch open and short faults and locate the defective switches using a single mathematical computation. The proposed algorithm is tested by simulation in MATLAB/Simulink and experimentally validated using the LabVIEW hardware-in-the-loop platform. The results demonstrate the robustness and effectiveness of the proposed technique in identifying and locating faults.

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31

Jing, Liuming, Tong Zhao, Lei Xia, and Jinghua Zhou. "An Improved High-Resistance Fault Detection Method in DC Microgrid Based on Orthogonal Wavelet Decomposition." Applied Sciences 13, no. 1 (December 28, 2022): 393. http://dx.doi.org/10.3390/app13010393.

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High-resistance faults in direct current (DC) microgrids are small and thus difficult to detect. Such faults may be “invisible” in that grid operation continues for a considerable time, which damages the grid. It is essential to detect and remove high-resistance faults; we present a detection method herein. First, the transient DC current during the fault is subjected to hierarchical wavelet decomposition to identify high-resistance faults accurately and sensitively; the wavelet coefficients are detected using the singular value decomposition (SVD) method. The SVD valve can denoise the dc microgrid fault current, which eliminates the influence of converter switching frequency and background noise effectively. Power system computer-aided design (PSCAD)/electromagnetic transients including direct current (EMTDC)-based simulations showed that our method successfully identified high-resistance faults.

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32

Jammazi, Rania. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach." Energy 37, no. 1 (January 2012): 430–54. http://dx.doi.org/10.1016/j.energy.2011.11.011.

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33

Tan, Kuang-Hsiung, and Tzu-Yu Tseng. "Seamless Switching and Grid Reconnection of Microgrid Using Petri Recurrent Wavelet Fuzzy Neural Network." IEEE Transactions on Power Electronics 36, no. 10 (October 2021): 11847–61. http://dx.doi.org/10.1109/tpel.2021.3066986.

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34

Thorat, Prachi. "Detection of Capacitor Switching Transients and L-G Fault Transients by Implementing Wavelet Transform." IOSR Journal of Electrical and Electronics Engineering 2, no. 5 (2012): 39–42. http://dx.doi.org/10.9790/1676-0253942.

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35

Gao, Y., Q. Chen, S. Tse, and X. Xu. "A wavelet approach to determine the switching frequency for composite control during surface grinding." Journal of Materials Processing Technology 129, no. 1-3 (October 2002): 480–84. http://dx.doi.org/10.1016/s0924-0136(02)00619-2.

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36

Yang, Tao, XiRen Miao, and ShengBin Zhuang. "Fast detection of short circuits in low-voltage AC systems based on wavelet packet transform and current change rate." Journal of Physics: Conference Series 2564, no. 1 (August 1, 2023): 012031. http://dx.doi.org/10.1088/1742-6596/2564/1/012031.

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Abstract The safe and stable operation of a new low-voltage power system requires the research and application of fast detection technology of short-circuit faults. However, the existing methods are difficult to effectively realize the rapidity and reliability in case of short circuits with unclear features and various interference conditions. Therefore, the fault feature of short-circuit current in the full phase angle range is analyzed, and a new method of short-circuit detection based on wavelet packet transform and current change rate is proposed. Among them, the fast-action detection method of wavelet packet transform is used for faults with obvious features to achieve detection rapidity, and the double criteria backup detection method of wavelet packet transform and current change rate is used for faults with unclear features to ensure detection reliability. On this basis, the hardware technology realization of short-circuit fault fast detection algorithm is studied. For short circuit faults, load startup, load switching, and other situations, the real low-voltage AC system experiment is carried out to verify. Finally, the test shows that the detection method can effectively and rapidly detect short-circuit faults within 1ms in the full phase angle range, and has robustness under interference conditions.

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37

Wei, Hua Liang, and Stephen A. Billings. "A comparative study on global wavelet and polynomial models for non-linear regime-switching systems." International Journal of Modelling, Identification and Control 2, no. 4 (2007): 273. http://dx.doi.org/10.1504/ijmic.2007.016410.

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38

Lu, Yang, Nianyin Zeng, Yurong Liu, and Nan Zhang. "A hybrid Wavelet Neural Network and Switching Particle Swarm Optimization algorithm for face direction recognition." Neurocomputing 155 (May 2015): 219–24. http://dx.doi.org/10.1016/j.neucom.2014.12.026.

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Nagaraju, M., V. V. K. Reddy, and M. Sushama. "Wavelet based performance analysis of AC transmission systems with unified power flow controller under power quality issues." International Journal of Applied Power Engineering (IJAPE) 8, no. 3 (December 1, 2019): 299. http://dx.doi.org/10.11591/ijape.v8.i3.pp299-308.

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The developments in power quality are fast and difficult to predict. The majority of power quality issues experienced by industrial customers can be attributed to momentary interruptions, voltage sags or swells, transients, harmonic distortion, electrical noise, and flickering lights, among others. A new device may be invented tomorrow solving power quality problems. The FACTS devices could provide fast control of active and reactive power through a transmission line. The unified power-flow controller (UPFC) is a member of the FACTS family with very attractive features. This device can independently control many parameters, so it is the combination of the properties of a static synchronous compensator (STATCOM) and static synchronous series compensator (SSSC).The performance of AC Transmission system with Unified power flow controller under various power quality problems analysis described. The proposed system is formulated and research work is done by wavelet multi resolution analysis using Bior1.5 mother wavelet with MATLAB/SIMULINK software. It is observed that the effectiveness of AC power transmission through Unified power flow controller under power quality problems of sag, swell, transient, temporary faults and capacitive switching.

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Chang, Hanjui, Shuzhou Lu, Yue Sun, Guangyi Zhang, and Longshi Rao. "Optical Penetration and “Fingerprinting” Analysis of Automotive Optical Liquid Silicone Components Based on Wavelet Analysis and Multiple Recognizable Performance Evaluation." Polymers 15, no. 1 (December 25, 2022): 86. http://dx.doi.org/10.3390/polym15010086.

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The residual stress phenomenon in the injection process of an optical lens affects the quality of optical components, and the refractive error caused by geometric errors is the most serious, followed by the degradation of the accuracy and function of optical components. It is very important to ensure that the lens geometry remains intact and the refractive index is low. Therefore, a parameter design method for an optical liquid silicon injection molding was proposed in this study. Wavelet analysis was applied to the noise reduction and feature extraction of the cavity pressure/pressure retaining curve of the injection molding machine, and multiple identifiable performance evaluation methods were used to identify and optimize the parameters of the molding process. Taking an automotive LED lens as an example, Moldex3D simulation software was used to simulate the molding of an LED lens made of LSR material, and two key injection molding factors, melt temperature and V/P switching point, were analyzed and optimized. In this paper, the transmittance and volume shrinkage of LED lenses are taken as quality indexes, and parameters are optimized by setting different V/P switching points and melt temperature schemes. The experimental results show that the residual stress is negatively correlated with transmittance, and the higher the residual stress, the lower the transmittance. Under the optimum process parameters generated by this method, the residual stress of plastic parts is significantly optimized, and the optimization rate is above 15%. In addition, when the V/P switching point is 98 and the melt temperature is 30 °C, the product quality is the best, the volume shrinkage rate is the smallest, and the size is 2.895%, which also means that the carbon emissions are the lowest.

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Zhang, Huijuan, Jiancheng Fang, and Hu Liu. "Online current signal de‐noising of magnetic bearing switching power amplifier based on lifting wavelet transform." IET Electric Power Applications 10, no. 8 (September 2016): 799–806. http://dx.doi.org/10.1049/iet-epa.2015.0457.

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42

Khan, Faisal A., Mohammad Munawar Shees, Mohammed F. Alsharekh, Saleh Alyahya, Faisal Saleem, Vipul Baghel, Adil Sarwar, Muhammad Islam, and Sheroz Khan. "Open-Circuit Fault Detection in a Multilevel Inverter Using Sub-Band Wavelet Energy." Electronics 11, no. 1 (December 30, 2021): 123. http://dx.doi.org/10.3390/electronics11010123.

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Recent research has focused on sustainable development and renewable energy resources, thus motivating nonconventional cutting-edge technology development. Multilevel inverters are cost-efficient devices with IGBT switches that can be used in ac power applications with reduced harmonics. They are widely used in the power electronics industry. However, under extreme stress, the IGBT switches can experience a fault, which can lead to undesirable operation. There is a need for a reliable system for detecting switch faults. This paper proposes a signal processing method to detect open-circuit problems in IGBT switches. Relative wavelet energy has been used as a feature for a machine learning algorithm to diagnose and classify the faulted switches. The switching sequence can be altered to restore a healthy output voltage. Inverter faults have been diagnosed by using support vector machine (SVM) and decision tree (DT), and an ensemble model based on decision tree (DT) and XG boost algorithm was developed, which yielded 92%, 88%, and 94.12% accuracy, respectively.

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43

Kankale, Ravishankar Shaligram, Sudhir Ramdas Paraskar, and Saurabh Sureshrao Jadhao. "Classification of Power Quality Disturbances in Emerging Power System Using Discrete Wavelet Transform and K-Nearest Neighbor." ECS Transactions 107, no. 1 (April 24, 2022): 5281–91. http://dx.doi.org/10.1149/10701.5281ecst.

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This paper presents a wavelet and machine learning-based approach for the classification of power quality disturbances (PQDs) in emerging power systems. Renewable energy resources-based distributed generation (DG) is rapidly being used in the emerging power system to address the ever-increasing energy demand. PQDs are thought to be caused by the power electronic converters used in DG systems, DG operating conditions, and other common factors such as faults, switching activities, and non-linear loads. These PQDs must be detected and classified since they can create a variety of difficulties in end-user equipment. The proposed algorithm comprises the simulation of the emerging power system with a solar PV system, creating PQDs cases such as voltage sag, voltage swell, and voltage interruptions, capturing voltage signals which will be further processed using a discrete wavelet transform for feature extraction. The features extracted from DWT analysis are further used to develop the machine learning-based classifier for classification of PQDs. The proposed algorithm has been tested on a variety of PQDs. The simulation result shows that the proposed algorithm is efficient and it outperforms in the classification of PQDs.

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44

Mudiangombe, Benjamin Mudiangombe, and John Weirstrass Muteba Mwamba. "Dependence Structure and Time–Frequency Impact of Exchange Rates on Crude Oil and Stock Markets of BRICS Countries: Markov-Switching-Based Wavelet Analysis." Journal of Risk and Financial Management 16, no. 7 (July 3, 2023): 319. http://dx.doi.org/10.3390/jrfm16070319.

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This paper used the Markov-switching (MS)-based wavelet analysis technique to study the dependence structure and the time–frequency impact of exchange rates on crude oil prices (West Texas Intermediate (WTI)) and stock returns. Daily data from 1 January 2005 to 1 March 2020 were collected for exchange rates, crude oil prices, and the BRICS stock market returns. The findings indicate that crude oil prices display higher volatility compared to stock returns and exchange rates. Furthermore, the wavelet analysis reveals consistent changes in the co-movement patterns of both volatility regimes, albeit with some variations in the time periods and frequency domains. The time–frequency dependence between Brazilian, Indian, and Chinese stock markets and crude oil is significantly influenced by exchange rates, which play a pivotal role in their co-movement in the medium term. The findings reveal that these three countries share economic interests, have strong economic ties and interdependencies, and may be motivated to cooperate during crisis periods. However, when it comes to Russia and South Africa (SA), exchange rates do not exhibit a long-term impact on the co-movement in time–frequency. Therefore, we recommend investors to look for investment opportunities that are less correlated with the co-moving markets.

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45

Lv, Chun, Peilin Zhang, Dinghai Wu, Bing Li, and Yunqiang Zhang. "Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter." International Journal of Rotating Machinery 2020 (March 3, 2020): 1–8. http://dx.doi.org/10.1155/2020/7567439.

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Bearing fault signal analysis is an important means of bearing fault diagnosis. To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology. Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF). The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.

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46

Wang, Yangkun, Feng Zhang, Shiwen Zhang, and Guang Yang. "A novel diagnostic algorithm for AC series arcing based on correlation analysis of high-frequency component of wavelet." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 36, no. 1 (January 3, 2017): 271–88. http://dx.doi.org/10.1108/compel-08-2015-0282.

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Purpose A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection. Design/methodology/approach The proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected. Findings Typical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold. Practical implications The arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development. Originality/value This proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.

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Kumar, P., and K. N. Rai. "Numerical solution of generalized DPL model using wavelet method during thermal therapy applications." International Journal of Biomathematics 12, no. 03 (April 2019): 1950032. http://dx.doi.org/10.1142/s1793524519500323.

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In this paper, generalized dual-phase-lag (DPL) model has been studied for the numerical analysis of spatial variation of temperature within living biological tissues during thermal therapy applications. A new hybrid numerical scheme based on finite difference scheme and Chebyshev wavelet Galerkin method are used to solve the generalized DPL model with constant heat flux boundary condition. Multi-resolution and multi-scale computational property of Chebyshev wavelet in the present case localizes small scale variations of solution and fast switching of functional bases. Our study demonstrates that due to presence of coupling factor (convection–perfusion), generalized DPL model predicts lower temperature than classical DPL and Pennes model at the tumor position. Higher values of phase lag times results in lower temperature at the tumor position. But, in case of variation of phase lag time due to temperature gradient, the nature of temperature profile also depends on the spatial coordinate. The effect of the blood temperature, porosity and interfacial convective heat transfer on temperature distribution has been investigated. It is found that larger values of porosity and interfacial convective heat transfer results in lower temperature at the tumor position. Also, both porosity and interfacial convective heat transfer are pronounced more at higher values. The whole analysis is presented in dimensionless form.

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Tadakuma, T., M. Rogers, K. Nishi, M. Joko, and M. Shoyama. "Carrier Stored Layer Density Effect Analysis of Radiated Noise at Turn-On Switching via Gabor Wavelet Transform." IEEE Transactions on Electron Devices 68, no. 4 (April 2021): 1827–34. http://dx.doi.org/10.1109/ted.2021.3061492.

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Guo Jun, Guo Yecai, and Chen Qu. "Wavelet Hardware Switching Judgment Frequency Domain Weighted Multi-Modulus Blind Equalization Algorithm based on Lower Order Statistics." Journal of Convergence Information Technology 7, no. 22 (December 31, 2012): 686–93. http://dx.doi.org/10.4156/jcit.vol7.issue22.81.

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50

Yi, Tongqiang, Yanzhao Xie, Hongye Zhang, and Henan Liu. "Electric Field Signal Recognition Method of DS Switching Operations Based on Wavelet Packet Analysis and PSO-HSVM." Journal of Physics: Conference Series 1449 (January 2020): 012026. http://dx.doi.org/10.1088/1742-6596/1449/1/012026.

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