Signal Processing Deployment in Power Quality Disturbance Detection and Classification

Dekhandji, Fatma Zohra

doi:10.12693/aphyspola.132.415

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…18,19 • Characteristics of the electricity at a given point on an electrical system, evaluated against a set of reference technical parameters (IEC 61000- . 18,19 • Characteristics of the electricity at a given point on an electrical system, evaluated against a set of reference technical parameters (IEC 61000- .…”

Section: Power Qualitymentioning

confidence: 99%

“…• PQ is a concept of powering and grounding sensitive equipment in a matter that is suitable to the operation of that equipment (IEEE dictionary). 18,19 • Characteristics of the electricity at a given point on an electrical system, evaluated against a set of reference technical parameters (IEC 61000- . 20 • PQ is the combination of voltage quality and current quality.…”

Section: Power Qualitymentioning

confidence: 99%

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Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

130

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Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

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Section: Power Qualitymentioning

confidence: 99%

Section: Power Qualitymentioning

confidence: 99%

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

130

View full text Add to dashboard Cite

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“…In addition, WT is susceptible to being affected by sounds and the employed algorithm [10,11]. [12,13] F. Z. Dekhandji devised a power quality monitoring system based on abc-0dq transformation and 90 phase shift algorithms. N. Mohan investigated the use of gated recurrent units (GRU) and the convolutional neural network-long short-term memory (CNN-LSTM) and proposed an optimal architecture for deep learning with specific network parameters and topologies [14].…”

Section: Introductionmentioning

confidence: 99%

Detection of PQ Short Duration Variations using Stockwell Transform with LSTM

Ali,

Oda,

Abdelsalam

et al. 2023

Suez Canal Engineering, Energy and Environmental Science

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Within the electrical power system, classification, and detection of power quality disturbances (PQDs) are top priorities. We are utilizing feature extraction with artificial intelligence (AI) and deep learning to solve PQD problems utilizing a two-step technique: the feature extraction and categorization steps, with the Feature extraction step employing Stockwell Transform and the categorization step utilizing Long Short-Term Memory techniques. For the detection and classification of PQ disturbance occurrences, this study seeks to employ Stockwell Transform as a feature extraction method using the LSTM Deep Learning (DL) method. Signal characteristics are extracted from time-frequency analysis data utilizing the Stockwell transform and Deep Learning in the long short-term memory (LSTM) network, which detects and classifies PQ disturbance events. Combining the S-transform with the long short-term memory (LSTM) network enables a high level of classification efficiency. Numerous PQ disorders are treated with single and combination disruptions. The results indicate that the proposed method is accurate and reliable for identifying and recognising single and combination PQ disruptions.Compared to numerous concise studies, the proposed strategy performs exceptionally well.

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“…Moreover, WT is prone to be affected by noises and the algorithm that is utilized [13,14]. F. Z. Dekhandji developed a power quality monitoring system based on abc-0dq transformation and 90• phase shift algorithms [15,16]. N. Mohan studied gated recurrent units (GRU) and convolutional neural networklong short-term memory (CNN-LSTM), and proposed an optimal deep learning architecture with specific network parameters and topologies [17].…”

Section: Introductionmentioning

confidence: 99%

Research on Improved S Transform for the Feature Extraction of Power Quality Disturbance Signal

Guo

Wang

et al. 2020

IEEE Access

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As the widely use of electronic equipment and the limitation of distribution lines, power quality issues have been more and more critical, and quantifying the power quality, monitoring the power disturbances are getting more and more attention. It crucial for the electric compensation and the improvement of electrical safety to analyze the power quality issues and locate the disturbances. Because of the limitation that most time-frequency algorithms cannot take both time and frequency resolution into account, an improved S transform based on sigmoid function which is used to adjust window width factor is proposed to obtain the characteristics of electrical disturbances, such as occurrence time, magnitude, duration, harmonics and so on. By introducing evaluation index, to measure the effect of different methods on signal analysis. Through the simulation of different kinds of power quality disturbances, and comparing with the S transform, the improved S transform based on sigmoid has the advantages of high accuracy for the extraction of characteristics. Meanwhile, it meets the requirements of most power quality disturbances and the parameters setting are simple, hence the algorithm is convenient for applications. In practice, the effect of noise on power quality disturbance signal is effectively reduced by the improved S transform.

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Signal Processing Deployment in Power Quality Disturbance Detection and Classification

Cited by 13 publications

References 15 publications

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Detection of PQ Short Duration Variations using Stockwell Transform with LSTM

Research on Improved S Transform for the Feature Extraction of Power Quality Disturbance Signal

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