Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Li, Jinsong; Liu, Hao; Deng-ke, Wang; Bi, Tianshu

doi:10.3389/fenrg.2021.708131

Cited by 19 publications

(7 citation statements)

References 31 publications

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“…This solution requires the definition of a variable time window that must be established, but the classification accuracy can be improved. The use of an S-transform and a Convolution Neural Network (CNN) is proposed in [ 41 , 42 ], but the results show a miss-classification problem. A Support Vector Machine classifier is proposed in [ 43 ] with a good classification accuracy, but also in this case, some miss-classifications must be resolved.…”

Section: Central Classification Unitmentioning

confidence: 99%

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Carní,

Lamonaca

2024

Sensors

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The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quality (PQ) evaluation. In this scenario, an Automatic PQ Events Classifier (APQEC) that detects, segments, and classifies the anomaly in the power signal is needed for the timely intervention and maintenance of the grid. Due to the extension and complexity of the network, the number of points to be monitored is large, making the cost of the infrastructure unreasonable. To reduce the cost, a new architecture for an APQEC is proposed. This architecture is composed of several Locally Distributed Nodes (LDNs) and a Central Classification Unit (CCU). The LDNs are in charge of the acquisition, the detection of PQ events, and the segmentation of the power signal. Instead, the CCU receives the information from the nodes to classify the PQ events. A low-computational capability characterizes low-cost LDNs. For this reason, a suitable PQ event detection and segmentation method with low resource requirements is proposed. It is based on the use of a sliding observation window that establishes a reasonable time interval, which is also useful for signal classification and the multi-sine fitting algorithm to decompose the input signal in harmonic components. These components can be compared with established threshold values to detect if a PQ event occurs. Only in this case, the signal is sent to the CCU for the classification; otherwise, it is discarded. Numerical tests are performed to set the sliding window size and observe the behavior of the proposed method with the main PQ events presented in the literature, even when the SNR varies. Experimental results confirm the effectiveness of the proposal, highlighting the correspondence with numerical results and the reduced execution time when compared to FFT-based methods.

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Section: Central Classification Unitmentioning

confidence: 99%

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Carní,

Lamonaca

2024

Sensors

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“…A remarkable improvement provided by deep learning, and the main motivation to apply these types of algorithms to PQD detection and classification, is the ability of models to automatically extract the best set of features from raw data to conduct classification. Convolutional Neural Networks (CNN) have been widely used in multiple PQDs and voltage sag classification [103,111,116,119,121,125,130,131,158,164,167,171,179,183]. Other deep learning models have been particularly used in the classification of voltage sags according to the root causes, e.g., deep feedforward ANNs [124], Long Short-Term Memory (LSTM) [129,159,180] (2023) 8:3 [165,175], and independently recurrent neural networks [182].…”

Section: Deep Artificial Neural Network (Anns)mentioning

confidence: 99%

A systematic review of real-time detection and classification of power quality disturbances

Navarro

Agudelo-Martínez²,

Trujillo

et al. 2023

Prot Control Mod Power Syst

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This paper offers a systematic literature review of real-time detection and classification of Power Quality Disturbances (PQDs). A particular focus is given to voltage sags and notches, as voltage sags cause huge economic losses while research on voltage notches is still very incipient. A systematic method based on scientometrics, text similarity and the analytic hierarchy process is proposed to structure the review and select the most relevant literature. A bibliometric analysis is then performed on the bibliographic data of the literature to identify relevant statistics such as the evolution of publications over time, top publishing countries, and the distribution by relevant topics. A set of articles is subsequently selected to be critically analyzed. The critical review is structured in steps for real-time detection and classification of PQDs, namely, input data preparation, preprocessing, transformation, feature extraction, feature selection, detection, classification, and characterization. Aspects associated with the type of disturbance(s) addressed in the literature are also explored throughout the review, including the perspectives of those studies aimed at multiple PQDs, or specifically focused on voltage sags or voltage notches. The real-time performance of the reviewed tools is also examined. Finally, unsolved issues are discussed, and prospects are highlighted.

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“…Deep convolutional neural network (DCNN), as a deep learning method, has been a strong candidate for pattern recognition and image classification [29], [30]. In recent years, the DCNN has been used to process various types of data, such as three-dimensional (3D) data, pictures, and 1D signals [31]. In this paper, the DCNN is proposed for automatic feature extraction and the MSVM is used to classify of PQ disturbance signals.…”

Section: Proposed Classifiersmentioning

confidence: 99%

Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

et al. 2023

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In this paper, a novel approach to classify the signals of power quality (PQ) disturbance is proposed based on segmented and modified S-transform (SMST), deep convolutional neural network (DCNN), and multiclass support vector machine (MSVM). The idea of frequency segmentation with different adjustable parameters was used in the Gaussian window function. The accurate time-frequency localization and efficient feature extraction of different PQ disturbances then could be achieved. Firstly, the SMST was used to analyze the PQ disturbance signals and obtained two-dimensional (2D) contour maps with high time-frequency resolution. Then, the DCNN was employed to automatically extract features from the 2D contour maps. Finally, the MSVM classifier was developed for the classification of single and complex signals of PQ disturbance. In order to demonstrate the effectiveness and robustness of the proposed model, eight single and thirteen complex waveforms of PQ disturbances were considered without noise and with different noise level, respectively. Extensive simulations were performed and compared to other existing methods. The simulation results show that the proposed method has better performance than several stateof-the-art algorithms in classifying PQ disturbances under different noise level.

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Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Cited by 19 publications

References 31 publications

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

A systematic review of real-time detection and classification of power quality disturbances

Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

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