Generation of power quality disturbances using MATLAB-Simulink

Dhote, Paramanand V.; Deshmukh, B.T.; Kushare, B. E.

doi:10.1109/iccpeic.2015.7259479

Cited by 15 publications

(10 citation statements)

References 6 publications

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“…Other authors have designed a MATLAB application for monitoring the operation and power quality of electrical machines [5]. In [6] it is proposed the use of MATLAB/Simulink to simulate power quality disturbances in power systems, including three phase faults and switching of large load to simulate voltage sag, removing of large loads from system to simulate voltage swell, three phase nonlinear load to simulate harmonics at the load side, or switching of large capacitor bank to simulate voltage transient at the transmission bus, among others. On the other hand, some studies suggest that virtual laboratories can be a suitable option for learning power quality.…”

Section: Teaching and Learning Power Qualitymentioning

confidence: 99%

Understanding Power Quality using IoT-based Smart Analyzers and Advanced Software Tools

Alcayde¹,

Arrabal-Campos²,

González³

et al. 2021

REPQJ

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Power Quality is an important topic for undergraduate electrical engineering students around the world. In addition to the theoretical contents prepared and explained by the lecturer to their students, this matter has an important practical focus. In this paper, a framework for teaching power quality in laboratories using IoT-based smart analyzers and advanced software tools is developed to provide the students the opportunity of studying real data with a high level of detail. In particular, practical lessons have been designed in such a way that the students are trained in the use of well-known commercial smart meters (like the Circutor MYeBOX 1500) or opensource systems (like the openZmeter) to acquire energy and power quality data from real world measurements and to analyze the data collected using advanced software tools (like PowerVision). The results obtained from several courses of electrical and electronic engineering show that the students acquire practical skills that allow them to reinforce their knowledge regarding power quality concepts, including harmonics, and power quality events such as voltage sag/swell, flicker, or waveform distortions. Therefore, this methodology can be applied for teaching power quality in undergraduate and graduate electrical engineering courses.

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Section: Teaching and Learning Power Qualitymentioning

confidence: 99%

Understanding Power Quality using IoT-based Smart Analyzers and Advanced Software Tools

Alcayde¹,

Arrabal-Campos²,

González³

et al. 2021

REPQJ

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“…To create the mentioned model, signal samples are simulated with the Simulink toolbox in MATLAB with the help of different circuits [63], mimicking the real condition in the distribution network. The sampling time for all the samples is set to 5 ls, and 500 samples of normal and 500 samples of abnormal waveforms, equally spread in different five categories with 0.2 s length are generated.…”

Section: System Modelmentioning

confidence: 99%

Online power quality disturbance detection by support vector machine in smart meter

Parvez

Aghili

Sarwat

et al. 2018

J. Mod. Power Syst. Clean Energy

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Power quality assessment is an important performance measurement in smart grids. Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters. Addressing this issue, in this study, we propose segregation of the power disturbance from regular values using one-class support vector machine (OCSVM). To precisely detect the power disturbances of a voltage wave, some practical wavelet filters are applied. Considering the unlimited types of waveform abnormalities, OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data. This model is able to automatically detect the existence of any types of disturbances in real time, even unknown types which are not available in the training time. In the case of existence, the disturbances are further classified into different types such as sag, swell, transients and unbalanced. Being light weighted and fast, the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring. The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

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“…Various types of faults, improved nonlinear loads and operations of power system often create power quality disturbances. Any voltage, current or frequency deviations manifested case which lead to equipment failure or disoperation is power quality problem [3]- [5]. Poor power quality can cause problems such as reduce the lifetime of the load, instabilities and interruptions in production as well as fatal costs due to downtime of the equipment as indicated by IEEE Std.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Modeling and Analysis of Voltage Variation by Using Spectrogram

et al. 2018

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In this paper, the power quality (PQ) disturbance which is the voltage variations consist of voltage swell, sag and interruption are model and analyze. Different types of voltage variations PQ disturbances models are developed and created by using MATLAB/Simulink as well as mathematical models. The mathematical and Simulink model are used to compare in terms of time-frequency representation (TFR). The Simulink models include shutting down enormouscapacities from system to resemble voltage swell, large loads energizing and three-phase fault to simulate voltage sag as well as implementing permanent three-phase fault to simulate voltage interruption. The signals generated are analyzed by using linear time-frequency distribution (TFD). The signal parameters such as root mean square voltage (Vrms), total harmonic distortion (THD) and power value are estimated from the TFR to identify the characteristics of the voltage variation. The results of analysis on the PQ disturbance waveforms generated are identical to the actual real-time PQ signals and the models can be modified to any desired situation respectively. The PQ waveforms obtained are suitable to be further analyzed.

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Generation of power quality disturbances using MATLAB-Simulink

Cited by 15 publications

References 6 publications

Understanding Power Quality using IoT-based Smart Analyzers and Advanced Software Tools

Understanding Power Quality using IoT-based Smart Analyzers and Advanced Software Tools

Online power quality disturbance detection by support vector machine in smart meter

A Comparative Modeling and Analysis of Voltage Variation by Using Spectrogram

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