Integrated DWT–FFT approach for detection and classification of power quality disturbances

Deokar, Sanjay A.; Waghmare, L. M.

doi:10.1016/j.ijepes.2014.04.015

Cited by 97 publications

(61 citation statements)

References 20 publications

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“…In reviewed literature [17,28,[39][40][41]45,46], computer models of treated PQD are obtained by using parametric descriptions as shown in Table 1, which provides the mathematical descriptions for the corresponding real-life phenomenon. Table 1, A and ω are the amplitude and angular frequency of the signal, respectively.…”

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

“…It is worth noticing that in [41] classes D and E of the disturbances are considered as the same one; this is indicated with (*). On the other hand, [17] is the only work that considers disturbance H and I separately from other classes. The analyzed signal for each PQD was obtained by describing its corresponding mathematical definition, given in Table 1, using MATLAB.…”

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

“…On the other hand, there are few methodologies that consider time-frequency analysis allowing the detection and classification of two or more PQD [12][13][14][15][16][17][18][19][20][21][22]. For instance, in [23], a research on voltage fluctuation and flicker measurement based on fast Fourier transform (FFT), is proposed.…”

Section: Introductionmentioning

confidence: 99%

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EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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Abstract:In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

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Section: Computer Simulation Of Pqdmentioning

confidence: 99%

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

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EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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“…Finally, to evaluate the practical performance of the proposed algorithm, a comparison among each method in Table 4 is performed, except for [4,9,10,22,23], by using measured data. The classification accuracy of [6,14,15] for measured data is 98.3, 96.3, and 95.2%, respectively, whereas the classification accuracy of this study is 98.6%.…”

Section: Performance Comparison and Discussionmentioning

confidence: 99%

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Liu¹,

Liu²,

Xu³

2016

Journal of Electrical Engineering and Technology

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-This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

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“…Furthermore, most PQ research concentrates on the assessment, detection, identification, location, and improvement of PQ [8][9][10][11][12][13][14]. PQ assessment can be used to make comprehensive judgments on the status of PQ over a historical period, but it fails to assess PQ status in the future.…”

Section: Introductionmentioning

confidence: 99%

Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms

Bai

Yuan³

et al. 2015

Energies

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Wind farms can affect the power quality (PQ) of the power supply grid, with subsequent impacts on the safe and stable operation of other electrical equipment. A novel PQ prediction, early warning, and control approach for the common coupling points between wind farms and the network is proposed in this paper. We then quantify PQ problems and provide rational support measures. To obtain predicted PQ data, we first establish a trend analysis model. The model incorporates a distance-based cluster analysis, probability distribution analysis based on polynomial fitting, pattern matching based on similarity, and Monte Carlo random sampling. A data mining algorithm then uses the PQ early warning flow to analyze limit-exceeding and abnormal data, quantify their severity, and output early warning prompts. Finally, PQ decision support is applied to inform both the power suppliers and users of anomalous changes in PQ, and advise on corresponding countermeasures to reduce relevant losses. Case studies show that the proposed approach is effective and feasible, and it has now been applied to an actual PQ monitoring platform.

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Integrated DWT–FFT approach for detection and classification of power quality disturbances

Cited by 97 publications

References 20 publications

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms

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