Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm

Hooshmand, Rahmat‐Allah; Enshaee, Ali

doi:10.1016/j.epsr.2010.07.001

Cited by 122 publications

(65 citation statements)

References 12 publications

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“…The treated cases of study in this paper consider just short-duration disturbances according to the IEC and IEEE standards (16.66 ms-150 ms, 110%-180% and 10%-90% in swell and sag) [47,48], this is depicted in Figure 6. The probability that distribution signals in real power systems will be contaminated with noise is quite high [44,46]; therefore, Gaussian noise contamination at different SNR is considered in this work. The SNR is given as:…”

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

“…The probability that distribution signals in real power systems will be contaminated with noise is quite high [44,46]; therefore, Gaussian noise contamination at different SNR is considered in this work. The SNR is given as:…”

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

“…In reviewed literature [17,28,[39][40][41]45,46], computer models of treated PQD are obtained by using parametric descriptions as shown in Table 1, which provides the mathematical descriptions for the corresponding real-life phenomenon. Table 1, A and ω are the amplitude and angular frequency of the signal, respectively.…”

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

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EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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Abstract:In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

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Section: Computer Simulation Of Pqdmentioning

confidence: 99%

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

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EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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“…Actually, in literature, a diversity of papers can be found concerning detection and identification of power quality disturbances by applying intelligent systems, such as Artificial Neural Networks (ANN) (Janik & Lobos, 2006;Oleskovicz et. al., 2009;Jayasree, Devaraj & Sukanesh, 2010) and Fuzzy Inference Systems (Zhu, Tso & Lo, 2004;Hooshmand & Enshaee, 2010;Meher & Pradhan, 2010;Behera, Dash & Biswal, 2010). However, only some papers use data pre-processing tools before the application of intelligent systems.…”

Section: Introductionmentioning

confidence: 99%

“…According to the literature, it should also be mentioned that the pre-processing tools help to ensure a better detection and identification of disturbances in the power quality context. In Hooshmand & Enshaee (2010), the authors propose a new method for detecting and classifying power quality disturbances. However, this method can be used both for the occurrence of one and multiple disturbances.…”

Section: Introductionmentioning

confidence: 99%

Pre-Processing Tools and Intelligent Systems Applied to Power Quality Analysis

Fernandes¹,

Rabêlo²,

Barbosa³

et al. 2011

Power Quality Â€“Â Monitoring, Analysis and Enhancement

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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

137

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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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Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm

Cited by 122 publications

References 12 publications

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

Pre-Processing Tools and Intelligent Systems Applied to Power Quality Analysis

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

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