Power quality disturbance classification using a statistical and wavelet-based Hidden Markov Model with Dempster–Shafer algorithm

Dehghani, Hesam; Vahidi, B.; Naghizadeh, Ramezan Ali; Hosseinian, Seyed Hossein

doi:10.1016/j.ijepes.2012.11.005

Cited by 78 publications

(33 citation statements)

References 40 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…Previous studies have carried out a lot of in-depth research on TFA of PQ signals, including Hilbert-Huang transform (HHT) [5,6], S-transform (ST) [7][8][9] and discrete wavelet transform (DWT) [10][11][12]. In the current research results, the environmental noise is the main factor which affects the PQ classification accuracy, especially in the distribution network.…”

Section: Introductionmentioning

confidence: 94%

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Huang

Cai

et al. 2016

Entropy

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Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.

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“…The well-known application of the DWT is to detect, characterize and locate power system transients [10]. Much research efforts have focused on wavelet-based techniques applied on analyzing power system transients [9], detecting and classifying PQ disturbances [11][12][13][14][15][16][17] and faults [18][19][20]. The start and end times of voltage sags and faults were also detected by means of the wavelet transform analysis [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A novel wavelet transform based voltage sag/swell detection algorithm

Latran

Teke

2015

International Journal of Electrical Power & Energy Systems

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Power quality disturbance classification using a statistical and wavelet-based Hidden Markov Model with Dempster–Shafer algorithm

Cited by 78 publications

References 40 publications

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

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

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

A novel wavelet transform based voltage sag/swell detection algorithm

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