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1.
《International Journal of Hydrogen Energy》2022,47(45):19821-19836
Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match. 相似文献
2.
This paper investigates a renewable energy resource’s application to the Load–Frequency Control of interconnected power system. The Proportional-Integral (PI) controllers are replaced with Proportional-Integral Plus (PI+) controllers in a two area interconnected thermal power system without/with the fast acting energy storage devices and are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network (BWNN) approaches. The energy storing devices Hydrogen generative Aqua Electroliser (HAE) with Fuel cell and Redox Flow Battery (RFB) are incorporated to the two area interconnected thermal power system to efficiently damp out the electromechanical oscillations in the power system because of their inherent efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE and RFB designed with BWNN controller reveals that the PI+ controller designed using BWNN approach is found to be superior than that of output response obtained using PI+ controller. Moreover the BWNN based PI+ controller exhibits a better transient and steady state response for the interconnected power system with Hydrogen generative Aqua Electroliser (AE) unit than that of the system with Redox Flow Battery (RFB) unit. 相似文献
3.
Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper. 相似文献
4.
Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis. 相似文献
5.
A stochastic model for local disturbances, particularly for a temporal harmonic with random modulations in amplitude and/or phase, is proposed in this paper. Results for the second moment responses of a linear single-degree-of-freedom system to this type of stochastic loading demonstrate a significant change in response characteristics due to a small uncertainty. A local phenomenon may last much longer and resonance may be smeared to a broad range. Integrated with wavelet transform, the proposed approach may be used to model a random process with non-stationary frequency content. Especially, it can be effectively used for Monte Carlo simulation to generate large size of samples that have similar characteristics in time and frequency domains as a pre-selected mother sample has. The technique has a great potential for the case where uncertainty study is warranted but the available samples are limited. 相似文献
6.
7.
基于子波变换的涡街流量传感器信号分析 总被引:6,自引:0,他引:6
长期以来 ,如何提取潜在噪声下的涡街流量信号一直是个问题。流体流速脉动 ,局部阻力 ,随机振动———所有这些因素都给解决这一问题带来难度。文章应用子波变换消噪理论 ,从软件滤波的角度分析了强噪声干扰下的涡街流量信号 ,并提出了单支重构计数方法。分析结果表明 ,这种方法对低流速流量计量效果很好 ,能够有效地扩展量程下限 相似文献
8.
Dantong Yu Gholamhosein Sheikholeslami Aidong Zhang 《Knowledge and Information Systems》2002,4(4):387-412
Finding the rare instances or the outliers is important in many KDD (knowledge discovery and data-mining) applications, such
as detecting credit card fraud or finding irregularities in gene expressions. Signal-processing techniques have been introduced
to transform images for enhancement, filtering, restoration, analysis, and reconstruction. In this paper, we present a new
method in which we apply signal-processing techniques to solve important problems in data mining. In particular, we introduce
a novel deviation (or outlier) detection approach, termed FindOut, based on wavelet transform. The main idea in FindOut is to remove the clusters from the original data and then identify
the outliers. Although previous research showed that such techniques may not be effective because of the nature of the clustering,
FindOut can successfully identify outliers from large datasets. Experimental results on very large datasets are presented
which show the efficiency and effectiveness of the proposed approach.
Received 7 September 2000 / Revised 2 February 2001 / Accepted in revised form 31 May 2001
Correspondence and offprint requests to: A. Zhang, Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA.
Email: azhang@cse.buffalo.eduau 相似文献
9.
Various image processing applications exploit a model of the human visual system (HVS). One element of HVS-models describes the masking-effect, which is typically parameterized by psycho-visual experiments that employ superimposed sinusoidal stimuli. Those stimuli are oversimplified with respect to real images and can capture only very elementary masking-effects. To overcome these limitations a new psycho-visual test method is proposed. It is based on natural scenery stimuli and operates in the wavelet domain. The collected psycho-visual data is finally used to evaluate the performance of various masking models under conditions as found in real image processing applications like compression. 相似文献
10.