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This paper presents a wavelet based technique for monitoring and measuring nonstationary power system disturbances. A significant improvement in monitoring efficiency is achieved by processing signals through Kaiser's window. This improvement is characterized by sparsity, separation, super-resolution, and stability. The maximum expansion coefficien...
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... Basically, non-stationary signals (having multiple frequencies ranging from 300 to 1000 Hz such as oscillatory-transients, voltage spikes, multiple voltage notches due to solid-state converter switching and harmonics) characterized by wide range of frequency spectrum with transient and sub-harmonic components are difficult to analyze as in [24]. These disturbances can be monitored as in [25] and classified on the basis of time-variant statistical characteristics of the voltage and current waveforms as in [26][27][28] and they could be sinusoidal or non-sinusoidal as in [14]. For only non-stationary PQ events, dominating frequency components have been used as features for recognition of events in [29]. ...
Power quality (PQ) interest has increasingly evolved over the past decade. The paper surveys the application of signal processing, intelligent techniques and optimization techniques in PQ analysis. This paper carries out a comprehensive review of articles that involves a comprehensive study of signal processing techniques used for PQ analysis. Within this context intelligent techniques such as fuzzy logic, neural network and genetic algorithm as well as their fusion are reviewed. Tabular presentation (i.e. highlighting the important techniques) has also been provided for comprehensive study. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested in the domain of PQ and wish to explore the opportunities offered by these techniques for further improvement in the field of PQ. Crown Copyright
... As a famous method of time frequency feature representation, the WT is much greater compact support for analysis of signals with localized transient components arising in the signal analysis [7][8][9][10]. WT also exhibits some disadvantages, such as its complicated computation, sensitivity to noise level, and the dependency of its accuracy on the chosen basis wavelet [11][12][13][14][15][16]. ...
... It decomposes the signal into time scale representation rather than time-frequency representation. The wavelet transform has been explored extensively in various studies as an alternative to the STFT [6][7][8][9][10][11][12][13][14]. ...
... Basically, non-stationary signals (having multiple frequencies ranging from 300 to 1000 Hz such as oscillatory-transients, voltage spikes, multiple voltage notches due to solid-state converter switching and harmonics) characterized by wide range of frequency spectrum with transient and sub-harmonic components are difficult to analyze as in [24]. These disturbances can be monitored as in [25] and classified on the basis of time-variant statistical characteristics of the voltage and current waveforms as in [26][27][28] and they could be sinusoidal or non-sinusoidal as in [14]. For only non-stationary PQ events, dominating frequency components have been used as features for recognition of events in [29]. ...
... [3], [4]. In the second approach, wavelet filter-based detection has been used [5]- [7]. A detection method using high order cumulants is proposed in [8]. ...
... overlap) of them yields a better detection of transition segment. It is worth noting that for the case of slow transition, (7) gives the middle point of the transition segment. The transition segment is considered as starting from the starting point of causal segment and ending at the starting point of anti-causal segment (i.e. the overlap between causal and anti-causal segments). ...
An important step in power disturbance data analysis is to first partition the disturbance sequence into transition and event segments. One main contribution of the paper is the propose of a new segmentation scheme that jointly uses the causal and anti-causal segmentation. We show that the actual location of a trigger point can be accurately estimated by combining the causal and anti-causal transition segments. Another main contribution is a new method for setting the threshold of detection parameter. From the residuals of Kalman filter, a detection parameter is defined and the threshold for this parameter is computed based on pdf estimates and hypothesis tests on measurement sequences with/without disturbances. Using the proposed segmentation and the statistically-based threshold, we show that transition segments, especially the trigger points, are allocated more accurately. Tests are performed on semi-synthetic disturbance sequences containing step, ramp and multi-stage transition. The proposed method is shown to be able to locate fast transitions with very high accuracy. The performance of segmentation against the point on wave, depth of transition, speed of transition, and polluted noise level are also evaluated.
Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its pre-eminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.
Non-stationary stochastic signal was divided into piecewise stationary stochastic signal, and reflecting the signal's characteristics by autocorrelation function of the piecewise stationary stochastic signal. Generally, the autocorrelation function was the base of selecting signal base for signal representation. For expressing non-stationary stochastic signal in a precise and effective way, based on the analysis of the natural characteristics of ARMA model and Markov process, a kind of multi-parameter estimation model of autocorrelation function for piecewise stationary stochastic process was proposed. The computational complexity was reduced, and the approximation effect was improved. Furthermore, the multi-parameter estimation model could also be adapted to the complex non-stationary stochastic signal, The computer simulation demonstrates that the approximation error was decreased significantly.
The nuclear signal processing is one of the key techniques in nuclear radiation detection. In this paper, the development of the nuclear signal processing using wavelet analysis at home and abroad was reviewed, the principle of wavelet transform and the conception of multi-resolution analysis were introduced. The nuclear signal processing based on wavelet analysis was discussed and the application of wavelet analysis on neutron/γ rays discrimination and spectrum data filtering were illustrated. With the development of the theory of wavelet analysis, it will be widely used in the field of nuclear signal processing.
The goal of this study is to develop a new tool for enhancing monitoring and modeling the quality of nonconductive coatings. The work is divided into two parts. In part 1, the time-frequency domain features using wavelet-based tool and the traditional Electrochemical Impendence Spectroscopy (EIS) measurements are integrated to enhance the monitoring process. In part 2, the modeling process of nonconductive coatings by integrating both tools is described. In this paper, enamel coatings on aluminum substrates are excited with high frequency voltage source of a square wave using three-electrode arrangement. Signals of non-stationary features of the current and voltage response of an enamel coated sample are analyzed using a wavelet-based tool. The rapid result extracted from the wavelet transform is used to detect and monitor any variation in the enamel coating and hence identify the locations where further investigation using EIS should be implemented. The proposed technique is implemented on large sets of laboratory data and shows promising results.
