Highly accurate detection of power quality disturbance using segmented and modified S-transform

Liu, Mingping; Chen, Yue; Zhang, Zhen; Deng, Suhui; Wang, Qingnian

doi:10.1016/j.epsr.2022.108664

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…Different future works can be suggested for the proposed research and architecture. The main ones are as follows: The development of LDN prototypes with different communication standards, both wired and wireless; The characterization of the system even in the presence of more than one PQ events at the same time; The product engineering of the LDN; The development of the CCU, making it capable of classify the PQ events and evaluating the characteristic parameters of the recognized event [ 53 , 54 , 55 ]; The development of a methodology and of a prototype suitable for three-phase systems. The literature analyzed shows few studies on unbalanced three-phase systems, in particular in the case of renewable energy sources [ 56 ].…”

Section: Future Workmentioning

confidence: 99%

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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: Future Workmentioning

confidence: 99%

“…The development of the CCU, making it capable of classify the PQ events and evaluating the characteristic parameters of the recognized event [ 53 , 54 , 55 ];…”

Section: Future Workmentioning

confidence: 99%

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

Carní,

Lamonaca

2024

Sensors

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“…According to the theories mentioned above, the SMST of a signal x(t) can be defined as [18] S SMST (τ, f )…”

Section: B Proposed Smstmentioning

confidence: 99%

“…Therefore, a new algorithm called segmented and modified S-transform (SMST) [18] is proposed and improved to overcome the disadvantages of the models mentioned above. The SMST divides the frequency spectrum of PQ disturbance signals into three bands i.e., low-, medium-, and high-frequency bands.…”

Section: Introductionmentioning

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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Quantizing branches' responsibility for voltage sags/swells and flicker events

Samet,

Wang,

Cuk

et al. 2023

Electric Power Systems Research

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Highly accurate detection of power quality disturbance using segmented and modified S-transform

Cited by 5 publications

References 33 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

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

Quantizing branches' responsibility for voltage sags/swells and flicker events

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