Power Quality Detection and Classification Using S-Transform and Rule-Based Decision Tree

Alqam, Salah J.; Zaro, Fouad

doi:10.18178/ijeetc.8.1.45-50

Cited by 19 publications

(7 citation statements)

References 8 publications

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“…It is remarkable the high impact of noise in the accuracy of the wavelet-based approaches [37,60]. The remaining works presented acceptable accuracy [15,38,39,41,43] but none was capable to deal with 13 PQDs as the actual proposal did. Only [15] achieved similar performances than those of the proposed CSO-QSVM approach.…”

Section: Results and Performance Comparisonmentioning

confidence: 99%

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Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study

2020

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The economic impact associated with power quality (PQ) problems in electrical systems is increasing, so PQ improvement research becomes a key task. In this paper, a Stockwell transform (ST)-based hybrid machine learning approach was used for the recognition and classification of power quality disturbances (PQDs). The ST of the PQDs was used to extract significant waveform features which constitute the input vectors for different machine learning approaches, including the K-nearest neighbors’ algorithm (K-NN), decision tree (DT), and support vector machine (SVM) used for classifying the PQDs. The procedure was optimized by using the genetic algorithm (GA) and the competitive swarm optimization algorithm (CSO). To test the proposed methodology, synthetic PQD waveforms were generated. Typical single disturbances for the voltage signal, as well as complex disturbances resulting from possible combinations of them, were considered. Furthermore, different levels of white Gaussian noise were added to the PQD waveforms while maintaining the desired accuracy level of the proposed classification methods. Finally, all the hybrid classification proposals were evaluated and the best one was compared with some others present in the literature. The proposed ST-based CSO-SVM method provides good results in terms of classification accuracy and noise immunity.

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Section: Results and Performance Comparisonmentioning

confidence: 99%

“…For its part, the rule-based DT classifier is a good choice when the features are clearly distinguishable from each other [40,41]. PQDs' classifiers based on DT include fuzzy decision tree [42][43][44] and the aforementioned classification and regression tree algorithm (CART) [17,21].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study

2020

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“…For a continuous time signal

, its ST of

is defined as [ 29 , 30 ]:

where,

is the Gaussian window function and

is the translation factor controlling the position of the Gaussian window on the time axis.…”

Section: Methodsmentioning

confidence: 99%

Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network

Luo

Jin

et al. 2020

Sensors

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As an important research direction of human–computer interaction technology, gesture recognition is the key to realizing sign language translation. To improve the accuracy of gesture recognition, a new gesture recognition method based on four channel surface electromyography (sEMG) signals is proposed. First, the S-transform is applied to four channel sEMG signals to enhance the time-frequency detail characteristics of the signals. Then, multiscale singular value decomposition is applied to the multiple time-frequency matrix output of S-transform to obtain the time-frequency joint features with better robustness. The corresponding singular value permutation entropy is calculated as the eigenvalue to effectively reduce the dimension of multiple eigenvectors. The gesture features are used as input into the deep belief network for classification, and nine kinds of gestures are recognized with an average accuracy of 93.33%. Experimental results show that the multiscale singular value permutation entropy feature is especially suitable for the pattern classification of the deep belief network.

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“…Several studies have been conducted on the detection and classification of power quality disturbances (PQDs). The detection algorithms for signal processing, such as Fourier transform [5][6][7], short-time Fourier transform (STFT) [8,9], wavelet transform (WT) [10,11], S-transform (ST) [12][13][14], Hilbert-Huang transform (HHT) [15], and chaos synchronization [16] are commonly used for power quality analysis. Artificial intelligence schemes containing support vector machines (SVM) [17,18], particle swarm optimization and support vector machines (PSO-SVM) [19], and neural networks [20][21][22] have been widely utilized for classifying power quality disturbances in accordance with the features extracted by signal processing algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Thirumala et al utilized a recognition technique that contains the tunable-Q wavelet transform (TQWT) and dual multiclass support vector machines (MSVM) for detection of PQDs [10]. Alqam and Zaro proposed a method based on S-transform and the rule-based decision tree for detection and recognition of PQDs under noiseless and noisy situations [12]. Sahani et al used an integrated intelligence method of Hilbert-Huang transform, and weighted bidirectional extreme learning machine (WBELM) with empirical mode decomposition (EMD) to detect and recognize PQDs [15].…”

Section: Introductionmentioning

confidence: 99%

Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis

Sian

Wang

et al. 2019

Applied Sciences

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The development of renewable energy and the increase of intermittent fluctuating loads have affected the power quality of power systems, and in the long run, damage the power equipment. In order to effectively analyze the quality of power signals, this paper proposes a method of signal feature capture and fault identification, as based on the extension neural network (ENN) algorithm combined with discrete wavelet transform (DWT) and Parseval’s theorem. First, the original power quality disturbance (PQD) transient signal was subjected to DWT, and its spectrum energy was calculated for each order of wavelet coefficients through Parseval’s theorem, in order to effectively intercept the eigenvalues of the original signal. Based on the features, the extension neural algorithm was used to establish a matter-element model of power quality disturbance identification. In addition, the correlation degree between the identification data and disturbance types was calculated to accurately identify the types of power failure. To verify the accuracy of the proposed method, five common power quality disturbances were analyzed, including voltage sag, voltage swell, power interruption, voltage flicker, and power harmonics. The results were then compared with those obtained from the back-propagation network (BPN), probabilistic neural network (PNN), extension method and a learning vector quantization network (LVQ). The results showed that the proposed method has shorter computation time (0.06 s), as well as higher identification accuracy at 99.62%, which is higher than the accuracy rates of the other four types.

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Power Quality Detection and Classification Using S-Transform and Rule-Based Decision Tree

Cited by 19 publications

References 8 publications

Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study

Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study

Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network

Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis

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