Power efficient signal conversion and quality signal compression using LDS-ADC and hybrid DCT for biomedical signals

The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, PQ signals were examined. There were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors of relative wavelet log-energy entropy were constructed. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.

Power quality (PQ) signals are the foundation of power quality analysis. Aim to get accurate analysis results, PQ signals should be recorded with high simple rate. Traditional power quality signal record methods with no data compression get a large amount of data set. It will cause the waste of memory space and difficult to transmit the data set. Wavelet transform is a very effective method for PQ signal compression. Therefore, this paper provides a status report of wavelet based PQ signals compression in the time-frequency domain through an overview of recent contributions. The results of the literature review indicate that power quality signals compression using wavelet transform is a very powerful tool and has been utilized in power system effectively. Wavelet-based compression methods using different types of wavelet transform and optimal wavelet thresholds have high compression ratio and reserve important details of original signals. The expectation is that further research and applications of wavelet will be widely applied in power quality signal compression.

Signal denoising is an important problem in the field of power quality (PQ) diagnosis of power system. Because the wavelet coefficients of noises are trending towards zero rapidly follow the scale increasing, the denoising methods based on wavelet transform have been widely applied. Threshold denoising method based on wavelet transform is an efficient method to reduce the white noise in the digital signal. There are two key problems need to be solved in practice. One is the optimization of the threshold; another is the determination of the decomposition order. This paper overviewed the theory of basis wavelet-based threshold denoising method and some kind improved soft-threshold theory, summarized their applications in the area of power quality signal denoising. All of results show wavelet-based threshold denoising method has great potential for PQ signals denoising. The analysis direction and emphasis of studying about the power quality (PQ) signal de-noising also put forward.

Wavelets are prominently used for Power Quality (PQ) signal analysis, the features that are computed from wavelet sub bands are informative for detection and classification. Energy levels of non-stationary events that occur in PQ signal computed considering wavelet sub bands suffer from shift variant property and hence use of dual tree complex wavelets that supports shift invariance property is used for PQ event analysis. In this paper, PQ event algorithm is developed considering dual tree wavelets and the results are compared with wavelets. Various PQ signals with non-stationary events are analyzed and the shift invariant property of dual tree wavelets is demonstrated to be advantageous in terms of event classification. Dual Tree Complex wavelet Transform (DTCWT) energy levels are capable of differentiating between multiple events as well as different types of sags, swells, harmonics, interrupts and flicker. The classification accuracy using DTCWT energy bands is improved by more than 90%. DTCWT filters selected in this paper are suitable for PQ event detection as well as classification. Article DOI: https://dx.doi.org/10.20319/mijst.2018.42.125136 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, there were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors were constructed from wavelet packet log-energy entropy of different nodes. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.

This paper introduces a high efficient compression method for power quality (PQ) signals. The proposed method makes use of the interpolated discrete Fourier transform algorithm for estimating the parameters (amplitude, frequency, and phase) of the fundamental and harmonic components and separates them from the transient ones in the original PQ signal. When these transient components are submitted to the compression technique, the sparse representation property of the wavelet transform (WT) provides an improvement in the compression ratio (CR). The experimental results show that the proposed method is suitable for various types of PQ signals and that it achieves a higher CR compared to the traditional WT-based compression techniques.

With the rapid development of modern science and continuous innovation of technology, a large number of power quality data storage and transmission has caused great burden to power system operation, so it is of great practical significance to study power quality. In order to solve the problem of the existing fixed orthogonal sparse basis is not enough to represent the unknown power quality signals flexibly when using the compression sensing theory to reconstruct power quality signals, which leads to poor signal reconstruction effect and poor applicability. K-SVD dictionary learning is introduced into the power quality signals reconstruction. Firstly, a large number of power quality signals samples are trained by K-SVD dictionary to get a super complete dictionary. Secondly, Gauss random matrix is selected as the measurement matrix. Lastly, KSVD-CoSaMP algorithm is proposed to reconstruct the power quality signals. Simulation experimental results show that, power quality signals are reconstructed using the proposed algorithm compared DCT algorithm, the SNR of all the reconstructed signals are higher than 30 dB, ERP are over 99.6%, and MSE are under 3%. The indexes of the proposed algorithm are obviously superior to the existing DCT algorithm, which further verifies the superiority and applicability of the proposed algorithm.

Power quality has become a greater concern nowadays. The increasing number of power electronics equipment contributes to the poor quality of electrical power supply. The power quality signals will affect manufacturing process, malfunction of equipment and economic losses. This paper presents the verification analysis of power quality signals classification system. The developed system is based on linear time-frequency distribution (TFD) which is spectrogram that represents the signals jointly in time-frequency representation (TFR). The TFD is very appropriate to analyze power quality signals that have magnitude and frequency variations. Parameters of the signal such as root mean square (RMS) and fundamental RMS, total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) of voltage signal are estimated from the TFR to identify the characteristics of the signal. Then, the signal characteristics are used as input for signal classifier to classify power quality signals. In addition, standard power line measurements are also calculated from voltage and current such as RMS and fundamental RMS voltage and current, real power, apparent power, reactive power, frequency and power factor. The power quality signals focused are swell, sag, interruption, harmonic, interharmonic, and transient based on IEEE Std. 1159-2009. The power quality analysis has been tested using a set of data and the results show that, the spectrogram gives high accuracy measurement of signal characteristics. However, the system offers lower accuracy compare to simulation due to the limitation of the system.

This paper presents an approach of wavelet transform based on multi-resolution analysis that is able to provide the detection and location as well as the identification of power quality problems present in transient and disturbed signals. The method was developed by using the given sample frequency to calculate the suitable decomposition level of power quality signals to optimized the program. The advantage of the proposed approach is to monitor the time of the occurrence and duration of the disturbances accurately. In this paper, some power quality signals with power quality disturbances have been analyzed to prove the accuracy of the method. In recent years, with a variety of sensitive electronic and power equipments widely used in industry, there is a problem of precision instruments and equipments in power system sensitive to power quality. Short-term voltage sags or disruption will lead computers or communication equipments to malfunction that may affect the normal work and cause heavy economic losses. Power quality disturbances are given more high attention. Transient power quality phenomenon mainly contain voltage sag, swell and short-term disruption which primarily are described by changes in voltage amplitude and duration of the transient disturbance. In order to find out the source of these signals, fast detection and location are required to be developed. The existing power quality detection methods can be broadly divided into two types: one is continuous wavelet transform and the other is multi-resolution analysis. The disadvantage of CWT lies in its larger calculation amount and greater redundancy. In this paper, to resolve the location problem of transient power quality signal, detection algorithm of local modulus maxima of wavelet transform has been proposed. When the transient signal suddenly changed, its wavelet transform coefficients have a modulus maxima through the signal decomposition by multi-resolution analysis. The detection of the modulus maxima can monitor the time of the occurrence and duration of the disturbances. The simulation results show that the method of multi-resolution decomposition can detect the location of transient power quality signal exactly. II. MULTI-RESOLUTION ANALYSIS

Compressed sensing technology breaks through the limitation of Nyquist sampling theorem, and can effectively reduce the cost of data storage and transmission. Only a few samples are needed to reconstruct the power quality signal by using the compressed sensing technology, which is of great significance for the detection and analysis of power quality. We propose an algorithm based on multitask Bayesian compressed sensing (MT-BCS) theory for power quality signals compression and reconstruction in this paper. The power quality signals are changed to sparse vectors by taking the fast Fourier transform basis as the sparse basis. The real and imaginary parts of sparse vectors are then treated as two compression and reconstruction tasks. Considering the internal correlation of data corresponding to these two tasks, the power quality signal is reconstructed using the sharing mechanism of hyperparameter estimation. The simulation results show that the algorithm is superior to the orthogonal matching pursuit algorithm and the Bayesian compressed sensing algorithm in anti-noise performance and reconstruction accuracy, and is more suitable for compressing and reconstructing power quality signals with complex disturbance.

Actual power quality signal which is often affected by noise pollution impacts the analysis results of the disturbance signal. In this paper, EEMD (Ensemble Empirical Mode Decomposition) -based threshold de-noising method is proposed for power quality signal with different SNR ( S ignal-to- N oise R atio ). A s a comparison, we use other four thresholds, namely, the heuristic threshold, the self-adaptive threshold, the fixed threshold and the minimax threshold to filter the noises from power quality signal. Through the analysis and comparison of three characteristics of the signal pre-and-post de-noised, including waveforms, SNR and MSE ( M ean S quare E rror), furthermore the instantaneous attribute of corresponding time by HHT (Hilbert Huang T ransform). Simulation results show that EEMD threshold de-noising method can make the wavefor m clos e to the actual value . T he SNR is high er and the MSE is small er c ompared wit h other four thresholds . T he instantaneous attribute can reflect the actual disturbance signal more exactly. The o ptimal threshold EEMD -based algorithm is proposed for power quality disturbance signal de-noising. Meanwhile, EEMD threshold de-noising method with adaptivity is suitable for composite disturbance signal de-noising.

Detection and classification of power quality (PQ) disturbances are an important consideration to electrical utility companies and many industrial customers so that diagnosis and mitigation of such disturbance can be implemented quickly. Power quality signal consists of stationary and non-stationary events which need a robust signal processing technique to analyse the signals. In this paper, Modified STransform (MST) was used to analyse single and multiple power quality signals. MST is a modified version of S-transform with improved time-frequency resolution. The power quality signals that are considered in this study are voltage swell, sag, interruption, harmonic, interharmonic, transient, sag plus harmonic and swell plus harmonics. The performance of the proposed method has been studied under noisy and unnoisy condition. Hard thresholding technique has been applied with MST while analysing noisy PQ signals. The result shows that MST is able to give higher classification rate with better time and frequency distribution (TFD) spectrum of the PQ disturbances.

This paper proposes the novel scheme of matched filter design for detection of power quality events in renewable integrated system. Matched filter gives better response on account of similarity with the signal, thus gives high SNR. Therefore, filters for power quality signals have been designed utilizing the information inherent in the power quality signal itself. In power quality signals, event-related information is hidden in the form of repetitive patterns in the signal. That repetitive information is extracted through the technique of dynamic histogram-based quantization for designing the filter matched with PQ disturbance signal. The stability of the newly designed matched filters has been analyzed using the frequency response of filters. The proposed scheme has been implemented for the detection of voltage sag, transient, and harmonics occurring in the renewable integrated system.KeywordsMatched filterHistogramRepetitive patternPower quality

Power quality analysis and governance need the identification of power quality issues. With the use of smart meters and various smart collection devices, more and more power quality data are collected, and the massive data collection brings pressure on communication, storage and computation to the conventional algorithm for identifying and classifying power quality disturbances based on cloud computing. In the paper, a classification algorithm for power quality disturbance identification based on edge computing and fusion model is proposed. The algorithm’s key concept is to compress and sense the power quality signals at the edge side, and then transmit the compressed power quality data to the cloud, which uses an improved Dense-Net and LSTM fusion model to identify and classify the compressed power quality data. Through experiments, it is proved that the method can compress the power quality signal to 70% of the original signal size while satisfying the recognition and data on power quality disturbance categorization accuracy, reducing the communication cost of data transmission, lowering the computational pressure and caching pressure on the cloud, and having certain robustness.

Power quality is the most important problems in power system automation. Aim to analysis and improve the power quality, many types of power quality events, such as voltage unbalanced, harmonic, frequency offset and multiple short time power quality disturbances, should be recorded accurately. This paper proposed a new design proposal of a novel digital fault recorder which could record the power quality waveform signals in 24 hours a day. The original power quality signals are transformed by fast Fourier transforms (FFT) and the waveform distortion is determined by the amplitude spectrum. In order to compression the data of power quality signals, the waveform without distortion is described by the first circle’s waveform. Hence, every power quality events signals and stationary signals will be recorded by one circle signal of each time. Then, the first circle signals of each event are compressed by wavelet transform so as to get higher compression ratio. The signal compressed by DFR will be stored in the Flash or RAM chips and transferred to principal computer. The data will be used for power quality analysis.