Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances

Andrade, Luciano Carli Moreira de; Oleskovicz, Mário; Fernandes, Ricardo Augusto Souza

doi:10.1016/j.asoc.2015.10.061

Cited by 47 publications

(28 citation statements)

References 24 publications

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“…From a PQ point of view, discussion of disturbances in transmission and distribution systems is important because environmental (lightning strokes) and load conditions strongly affect these two. The analysis of PQ disturbances starts from preprocessing in form of normalization and segmentation followed by feature extraction by applying some transformation such as FT, [7] STFT, [8] WT, [9][10][11][12] HT, [13] and S-transform. [14] Figure 12 shows numerous signals processing method whose mathematical representations are shown in Table 3.…”

Section: Signal Processing Methods For Pq Eventsmentioning

confidence: 99%

“…On every iteration, the amount of information is reduced up to a large extent. [23,10] The band pass filter extracts high frequency information with the disturbances such as transients and spikes and other sudden switching disturbances are detected, on the other hand low frequency components, the disturbance like harmonics can easily be detected. In ref.…”

Section: S Nomentioning

confidence: 99%

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Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Rahul

2020

Advcd Theory and Sims

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The issue of power quality (PQ) has become more prominent over the last few decades as the demand of clean and high quality power is increasing around the globe. The effect of power quality disturbances on the equipment most of the time is very destructive, usually generates disruptions, which consecutively affects the other load connected to the power systems. The main purpose of this article is to present a comprehensive review of various power quality analysis techniques such as heuristic optimization, signal processing, machine learning, neural networks, artificial intelligence, and hardware implementation, so that a brief overview will be presented to the researcher and power engineers working in the field of power quality. Additionally, a comparative analysis is also reported on various methods based on several criteria including timing and accuracy, use of industrial and non-industrial data set, noisy and noiseless conditions, and finally single and multiple power quality events for analysis. More than 200 research publications are included for analysis and listed in reference so that it will be easy for the researcher in the domain of power quality to explore the possibility of further improvement in this field.

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Section: Signal Processing Methods For Pq Eventsmentioning

confidence: 99%

Section: S Nomentioning

confidence: 99%

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Rahul

2020

Advcd Theory and Sims

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“…[3] power quality events are divided in steady state, which are long duration and non-occasional events which are short duration but WT deals with only steady state disturbances, in actual scenario short duration events are more prone to occur. An adaptive threshold based WT technique for localization [4,5] of single and two combined single events is developed which extracts starting-ending time locations and duration of events but still this technique lacks to detect complex power quality events. ST technique is also explored for detection of power quality events, can do the job of feature extraction with comparatively adequate time and frequency resolution and flexible window width feature of this technique makes it more efficient time resolution in high frequency parts of the signal and more frequency resolution in low frequency parts of the signal.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Power Quality Analysis: Adaptive FEM Algorithm with Sparse Signal Decomposition

Rahul

2020

Advcd Theory and Sims

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An adaptive approach of discretized triangular mesh based on finite element method hybrid with type‐2 fuzzy is utilized for detection and classification of power quality events. The concept of sparse signal decomposition is utilized with extraction of disturbances from power signals. An adaptive finite element method is then applied with the concept of stiffness matrix which extracts some relevant features of power quality events that act as input to type‐2 fuzzy classifiers. Finally performance comparison is carried out, the results of which show that the proposed technique has potential to analyze disturbances with high accuracy and minimum computational overhead.

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“…All these matters urgently demand the quick detection of any PQ issue in the interconnected system so that integration of renewable energy sources into the traditional grid can be made more robust and stable. For detection purpose, numerous signal processing techniques are in use, e.g., Fourier transform, short‐time Fourier transform (STFT), and wavelet transform (WT) . For detecting islanding and other PQ disturbances in grid connected environment, discrete WT is used as signal processing technique .…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

Saini

Beniwal

2018

Int Trans Electr Energ Syst

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The proposed work accomplishes detection and classification of simple and complex power quality disturbances occurring in the realm of wind-grid integrated system using fast time-time transform and small residual-extreme learning machine. Taking the advantage of time-time transform, this work has further tailored time-time transform by accommodating the dyadic scaling to make it a computationally less complex and faster technique. Here, threephase power quality signals are first segmented into single phases to be analyzed with fast time-time transform for getting the event information in timetime domain. The event information is further passed through two-stage feature extraction process to bring out twenty two characteristic curves; each of them is further used to compute five statistical features to produce 107 features after ignoring three redundant features. Thus, a high-dimensional feature set of size 321 is obtained corresponding to three phases of voltage signal. Because, high-dimensional feature set demands a robust classifier that can accurately classify the disturbances even in the presence of noise, small residual-extreme learning machine has been trained to classify the power quality signal into 12 classes (symmetrical sag, asymmetrical sag, swell, unbalance, harmonics, notch, momentary interruption, normal voltage signal, sag with harmonics, swell with harmonics, sag and notch with harmonics, and swell and notch with harmonics). This simulation-based study has been proved effective in dealing with noisy power quality signals also with reduced computational complexity and higher classification rate. KEYWORDS fast time-time transform, power quality, small residual-extreme learning machine classifier, wind-grid integrated system List of symbols and abbreviations: h(t), a time signal; w(t), Gaussian window; σ, standard deviation; f, frequency; β, output weight of SR-ELM network; G, output of hidden layer of SR-ELM network; X, input to SR-ELM; L, number of neurons in hidden layer of SR-ELM; M, number of neurons in output layer of SR-ELM; e, error; C. B.

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Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances

Cited by 47 publications

References 24 publications

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

A Novel Approach to Power Quality Analysis: Adaptive FEM Algorithm with Sparse Signal Decomposition

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

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