MATLAB/Simulink based modeling and simulation of power quality disturbances

Khokhar, Suhail; Zin, Abdullah Asuhaimi Mohd; Mokhtar, Ahmad Safawi; Ismail, Nam

doi:10.1109/cencon.2014.6967545

Cited by 32 publications

(18 citation statements)

References 16 publications

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“…The sudden changes in the operating conditions such as switching of capacitor banks and lightning may cause spikes or impulsive and oscillatory transients. The steady-state PQ disturbances such as harmonics, flickers, notches are created due to the applications of nonlinear loads and power electronic converters [3]. These PQ disturbances may cause huge cost due to equipment malfunction.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

215

111

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Section: Introductionmentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

215

111

View full text Add to dashboard Cite

“…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%

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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“…[9] At last but not the least natural reasons of voltage sag which are generally seen frequently are animals and trees coming in contact with the transmission lines, lightning strikes etc. Effects of voltage sag on the whole system are as follows- [11] 1. System losses increase.…”

Section: B Causes and Effectmentioning

confidence: 99%

Effects of Transformer Connection on Voltage Sag Characterization

2016

IJSR

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Day by day with the increase in demand of power supply, people are also becoming aware of power quality. Present generation wants advanced power plant design with the increment of power supply and also the power supply must be of good quality. So, to design power plant, power engineers need to concentrate on how the quality of supply can be improved along with fulfilling all the demands at the same time. In today's era, people are using so many electronic or sensitive devices as per comfort and the same will be happen in future also. These devices are one of the major causes of power quality disturbance. To maintain the quality of the power, engineers need to design their system in a way such that these devices can not seriously affect the quality of the power. In this paper disturbance of power quality due to system voltage profile disturbance is discussed. In this, voltage sag is taken as serious concern for hampered power quality. Voltage sag occurs due to fault in the system and transformer connections play an important role in this. For the analysis, software used is MATLAB/SIMULINK.

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MATLAB/Simulink based modeling and simulation of power quality disturbances

Cited by 32 publications

References 16 publications

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

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

Effects of Transformer Connection on Voltage Sag Characterization

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