Daubechies Db4 Research Articles

Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries

Energy Storage Systems (EES) are key to further increase the penetration in energy grids of intermittent renewable energy sources, such as wind, by smoothing out power fluctuations. In order this to be economically feasible; however, the ESS need to be sized correctly and managed efficiently. In the study, the use of discrete wavelet transform (Daubechies Db4) to decompose the power output of utility-scale wind turbines into high and low-frequency components, with the objective of smoothing wind turbine power output, is discussed and applied to four-year Supervisory Control And Data Acquisition (SCADA) real data from multi-MW, on-shore wind turbines provided by the industrial partner. Two main research requests were tackled: first, the effectiveness of the discrete wavelet transform for the correct sizing and management of the battery (Li-Ion type) storage was assessed in comparison to more traditional approaches such as a simple moving average and a direct use of the battery in response to excessive power fluctuations. The performance of different storage designs was compared, in terms of abatement of ramp rate violations, depending on the power smoothing technique applied. Results show that the wavelet transform leads to a more efficient battery use, characterized by lower variation of the averaged state-of-charge, and in turn to the need for a lower battery capacity, which can be translated into a cost reduction (up to −28%). The second research objective was to prove that the wavelet-based power smoothing technique has superior performance for the real-time control of a wind park. To this end, a simple procedure is proposed to generate a suitable moving window centered on the actual sample in which the wavelet transform can be applied. The power-smoothing performance of the method was tested on the same time series data, showing again that the discrete wavelet transform represents a superior solution in comparison to conventional approaches.

KLASIFIKASI JENIS DELPHINIDAE (LUMBA-LUMBA) DENGAN DIMENSI FRAKTAL MENGGUNAKAN METODE HIGUCHI DAN KNN (K-NEAREST NEIGHBOR)

The delphinidae family belongs to the Cetacean Order and is a member of the Odontocetes. The delphinidae family has characterized by the physical characteristics and frequency ranges of sound signals produced. Living in the sea and being a rare animal makes delphinidae very difficult to find and if we want to be classified, we have to capture and analyze the physicality of delphinidae. By using the fractal dimension we can analyze the sounds of the delphinidae family based on the characteristics of their sound signals to classify them. In this research, members of the delphinidae family will be classified using the Higuchi and K-Nearest Neighbor methods. By using 80 data, namely Common Dolphin 18 data, Killer Whale 20 data, Fraser's Dolphin 20 data, and Long-Finned Pilot Whale 22 data, the data used is .wav. In the first step, the Pre-Processing process will be carried out, then the feature extraction process will be carried out using the Discrete Wavelet Transform type mother wavelet Daubechies db4 wavelet with level 5 decomposition and Fast Fourier Transform. Then we will find the fractal dimension value using the Higuchi method. After obtaining the fractal dimension, the data will be divided into Training data and Testing data using k-cross validation with k value experiments namely 2, 4, 8, and 10. After the data is divided the data will be classified using K-Nearest Neighbor with an experimental K value. namely 1, 3, 5 and 7. In this study, the highest accuracy value was 82.5% with Kmax = 50, k = 8, and K = 3.Thus it can be concluded, the Higuchi and K-Nearest Neighbor methods can be used to classify members of the family delphinidae
 Keywords: Delphinidae, classification of member of family delphinidae, Higuchi fractal dimension, KNN

Metode Wavelet-MFCC dan Korelasi dalam Pengenalan Suara Digit

Voice is the sound emitted from living things. With the development of Automatic Speech Recognition (ASR) technology, voice can be used to make it easier for humans to do something. In the ASR extraction process the features have an important role in the recognition process. The feature extraction methods that are commonly applied to ASR are MFCC and Wavelet. Each of them has advantages and disadvantages. Therefore, this study will combine the wavelet feature extraction method and MFCC to maximize the existing advantages. The proposed method is called Wavelet-MFCC. Voice recognition method that does not use recommendations. Determination of system performance using the Word Recoginition Rate (WRR) method which is validated with the K-Fold Cross Validation with the number of folds is 5. The research dataset used is voice recording digits 0-9 in English. The results show that the digit speech recognition system that has been built gives the highest average value of 63% for digit 4 using wavelet daubechies DB3 and wavelet dyadic transform method. As for the comparison results of the wavelet decomposition method used, that the use of dyadic wavelet transformation is better than the wavelet package.

Experimental study and Shannon entropy analysis of pressure fluctuations and flow mode transition in fluidized dense phase pneumatic conveying of fly ash

The objective of this study was to relate experimental pressure fluctuation behavior to the transition in mode of flow observed in fluidized dense phase pneumatic conveying of fly ash. Shannon entropy and wavelet analysis were utilized to extract features of the flow regimes. Daubechies db4 wavelet analysis of pulsating air pressure revealed that the flow mechanism of fly ash in a fluidized dense phase possessed non-steady characteristics associated with gradual aeration of dunes along the direction of flow. Variations of Shannon entropy values along the length of the pipeline were assessed to determine the location at which the flow converted from dense to dilute phase mode. The effects of conveying parameters and specific power consumption on Shannon entropy and variations of the local power consumption coefficient are discussed.

Transient characteristics of fine powder flows within fluidized dense phase pneumatic conveying systems

The objective of this paper is to report the experimental findings related to the pressure fluctuations in the fluidized dense phase pneumatic conveying of fine powders in the pipeline. Investigation has also been carried out to understand the relation between pressure pulse characteristics and specific power consumption and pneumatic conveying parameters. In addition, the transition in mode of flow along the downstream of flow of fine powders has been assessed and discussed. The wavelet analysis (Daubechies db4 wavelet) of signals of air pulses revealed that the moving bed flow possesses the transient feature and pulsatile phenomenon characterized by multiple amplitudes and random frequencies. Variations of pulse structures along the length of pipeline have been observed due to occurring of frequent aeration and de-aeration of dunes during the conveying. Substantial variations in pulse structures have also been found with different types of bulk materials. Pulse velocities of air between two data points are found higher at high solid loading ratios leading to low voidage in case of dense phase flow.

A novel methodology for fault detection, classification and location in transmission system based on DWT & ANFIS

A novel and precise numerical relaying approach based on discrete wavelet transform (DWT), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented in this paper. Detection and categorisation of the faults are achieved using designed neural network (NN) which is trained by samples of voltage and current, procured from the simulated test system. However, for localizing the faults, ANFIS is employed. The summation values of discrete wavelet coefficients of 3-phases are extracted for training and testing of ANFIS model. Daubechies db4 (mother wavelet) is applied for decomposing 3-phase current samples. For validating the effectiveness of the present approach, it is checked on a 400 kV transmission system in MATLAB. The results obtained demonstrate that proposed method is effective in detecting, classifying and predicting faults position in the network.

Stationary versus bifurcation regime for standing wave central pattern generator

Abstract The purpose of this research is to show that the correlation analysis on surface electromyographic (sEMG) signals that originally confirmed existence of a standing wave central pattern generator (CPG) along the spine are reproducible despite evolution of the entrainment technique, different hardware and data collection protocol. Moreover, as major novelty of the present research, it is shown that this CPG can undergo “bifurcations,” here revealed by signal processing extrapolated towards the period-halving dynamical interpretation. The visually intuitive manifestation of the bifurcation is statistically confirmed—using bootstrap analysis—by a shift in the cross power spectral densities, consistently with the standing wave occurring on different subbands of the Daubechies DB3 wavelet decomposition of the sEMG signals.

Using wavelet-feedforward neural networks to improve air pollution forecasting in urban environments.

The paper presents the screening of various feedforward neural networks (FANN) and wavelet-feedforward neural networks (WFANN) applied to time series of ground-level ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10 and PM2.5 fractions) recorded at four monitoring stations located in various urban areas of Romania, to identify common configurations with optimal generalization performance. Two distinct model runs were performed as follows: data processing using hourly-recorded time series of airborne pollutants during cold months (O3, NO2, and PM10), when residential heating increases the local emissions, and data processing using 24-h daily averaged concentrations (PM2.5) recorded between 2009 and 2012. Dataset variability was assessed using statistical analysis. Time series were passed through various FANNs. Each time series was decomposed in four time-scale components using three-level wavelets, which have been passed also through FANN, and recomposed into a single time series. The agreement between observed and modelled output was evaluated based on the statistical significance (r coefficient and correlation between errors and data). Daubechies db3 wavelet-Rprop FANN (6-4-1) utilization gave positive results for O3 time series optimizing the exclusive use of the FANN for hourly-recorded time series. NO2 was difficult to model due to time series specificity, but wavelet integration improved FANN performances. Daubechies db3 wavelet did not improve the FANN outputs for PM10 time series. Both models (FANN/WFANN) overestimated PM2.5 forecasted values in the last quarter of time series. A potential improvement of the forecasted values could be the integration of a smoothing algorithm to adjust the PM2.5 model outputs.

Wavelet Threshold De- Noising for Mammogram Images

Digital mammograms are coupled with noise which makes de-noising a challenging problem. In the literature, few wavelets like daubechies db3 and haar have been used for de-noising medical images. However, wavelet filters such as sym8, daubechies db4 and coif1 at certain level of soft and hard threshold have not been taken into account for mammogram images. Therefore, in this study five wavelet filters namely: haar, sym8, daubechies db3, db4 and coif1 at certain level of soft and hard threshold have been considered. Later, peak signal to noise ratio and mean squared error values are calculated. From the obtained results, it can be concluded that db3 (46.44656 db for hard threshold and 43.80779 db for soft threshold) is more appropriate filter for de-noising mammogram images while compared with other wavelets filters.

Implementation of discrete wavelet transform-based discrimination and state-of-health diagnosis for a polymer electrolyte membrane fuel cell

This research investigates a new approach based on the discrete wavelet transform (DWT) that suitable for analyzing and evaluating output terminal voltage signal (OTVS) for discrimination analysis of a polymer electrolyte membrane fuel cell (PEMFC). Due to its ability for extracting information from the non-stationary and transient phenomena simultaneously in both time and frequency domain, the OTVS can be applied as source data in the DWT-based approach. By using the wavelet decomposition including the multi-resolution analysis (MRA) using the Daubechies wavelet (dB) as mother wavelet, the information on the electrochemical characteristics of a PEMFC can be extracted from the OTVS over a wide frequency range. Thus, the cells that have similar electrochemical characteristics can be eventually discriminated. In particular, this present research develops these investigations one step further by showing low-frequency components (approximation A n ) and high-frequency components (detail D n ) extracted from variable single cells with different electrochemical characteristics. Experimental results show that DWT-based approach is clearly appropriate for the reliable SOH diagnosis for a PEMFC. • The DWT is useful for analyzing signal with non-stationary and transient phenomena. • The output terminal voltage signal (OTVS) of a PEMFC is used as original signal. • The DWT-based multi-resolution analysis (MRA) with Daubechies dB3 wavelet is used. • An outstanding discrimination result among variable single cells is provided. • The proposed approach shows the clearness for the reliable SOH diagnosis of a PEMFC.

Implementing and Testing $d$– $q$ WPT-Based Digital Protection for Microgrid Systems

This paper presents the implementation and performance evaluation of a new digital protection for microgrid systems, The proposed digital protection is based on utilizing the wavelet packet transform (WPT) for extracting the first-level high-frequency subband contents present in the d-q-axis current components. The utilized WPT is realized by a half-band digital high-pass filter, whose coefficients are determined by the Daubechies db4 wavelet basis functions. The d-q WPT-based digital protection is implemented for offline and experimental performance evaluation. Offline tests are conducted using data collected from a simulation model of a microgrid located in Fermeuse, NL, Canada. The experimental tests are conducted on a laboratory microgrid system that is constructed as a scaled version of the Fermeuse microgrid. Several transient disturbances occurring in different parts of the test microgrid systems are investigated both offline and in experimentation. Performance results demonstrate significant abilities of the d-q WPT-based digital protection for fast, accurate, and reliable detection, classification, and response to different transient disturbances. In all tested faults, the developed digital protection is able to generate a trip signal in less than a quarter cycle based on the 60-Hz system operating frequency.

Adaptive Single-Pole Autoreclosure Scheme Based on Wavelet Transform and Multilayer Perceptron

Adaptive autoreclosing is a fast emerging technology for improving power system marginal sta-bility during faults. It avoids reclosing unto permanent faults and recloses unto transient faults only after the secondary arc has extinguished. The challenges that come with the application of the adaptive autoreclosing technology are enormous. To come to grips with these challenges, researchers have been proposing autoreclosure schemes which use artificial neural network (ANN), the reason being that ANNs have in recent years clearly demonstrated their ability in solving some long standing problems in power systems where conventional techniques have dif-ficulty. This paper proposes one such scheme for single-pole autoreclosure. The scheme uses multilayer perceptron artificial neural network which decides whether a fault is transient or per-manent based on the percentage of energy in detailed coefficients of Daubechies db4 mother wavelet of the power system faulted voltage. In the case of transient faults, the neural network further determines the optimal reclosure time. A multilayer perceptron neural network was de-veloped to suit the input signal adopted for the scheme and trained using the Levenberg-Marquardt back-propagation technique. The scheme was simulated using the Electromagnetics Transient Programme (EMTP) and MATHLAB software. The results of the simulation show that the proposed ANN-based adaptive single-pole autoreclosure (AdSPAR) scheme is capable of distinguishing between permanent and transient faults and in the case of the latter, predict opti-mal reclosure times.Keywords: Adaptive autoreclosure, Artificial neural networks, Autoreclosure, Signal processing, Stability, Wavelet transform

Detection of power disturbances using morphological gradient wavelet

This paper presents a novel algorithm, morphological gradient wavelet (MGW), for the detection of power disturbances. MGW is a nonlinear wavelet transform that involves morphological operation in the decomposition process and presents the gradient information in the output. Applied to power systems, MGW extracts the features of disturbances and detects their location and duration. A variety of power disturbances have been simulated to evaluate the validity of MGW. Moreover, the advantages of the new algorithm are also addressed in comparison with Daubechies DB4 wavelet transform (DB4).