Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review

Oubrahim, Zakarya; Benbouzid, Mohamed; Ouassaid, Mohammed

doi:10.3390/en16062685

Cited by 10 publications

(10 citation statements)

References 218 publications

(292 reference statements)

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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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“…The above case is due to three major factors. The prominent factors are (1) the ability to learn from its errors, (2) verifying the accuracy of its predictions/outputs, and (3) making the necessary adjustments accordingly [7]. In addition to the above, it is found that, generally, DL performance increases proportionally to the training dataset's volume.…”

Section: Introductionmentioning

confidence: 98%

“…With many inherent and associated issues in power systems, particularly in smart grids and microgrid environments, PQ disturbance monitoring and detection is indispensable for secure, reliable, well-controlled, and protective system operation [1]. An advanced monitoring system with proper techniques to analyze PQ events is required to meet the consumer's requirements for a quality power supply.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

et al. 2023

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Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods.

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“…One of the leading causes of poor power quality is the occurrence of anomalies within the supply signals, known as power quality disturbances (PQD). Identifying the occurrence of these phenomena in the supply systems is a critical step because poor PQ can cause equipment malfunction and damage, a decreasing AC motor speed, relay malfunction, and the downtime of computers, among others [8,9]. In addition, real-time PQD monitoring can help to take faster actions to mitigate PQ issues, and it is a way of knowing the PQD propagation along the grid [10].…”

Section: Introductionmentioning

confidence: 99%

Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances

Reyes-Archundia,

Yang,

Gutiérrez Gnecchi

et al. 2024

Energies

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Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm’s accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases.

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Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review

Cited by 10 publications

References 218 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 Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances

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