Voltage Sag Detection Based On Dq Transform and Mathematical Morphology Filter

Ouyang, Huajiang; Bu, Leping; Yang, Zhonglin

doi:10.1016/j.proeng.2011.11.2580

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…The voltage sag detection method by DQ transformation and mathematical morphological technique presented in ref. [103]. Three-phase voltage is converted to the DQ axis and is passed through MM filter to find other parameters such as phase angle change and distortion in magnitude.…”

Section: S Nomentioning

confidence: 99%

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Rahul

2020

Advcd Theory and Sims

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The issue of power quality (PQ) has become more prominent over the last few decades as the demand of clean and high quality power is increasing around the globe. The effect of power quality disturbances on the equipment most of the time is very destructive, usually generates disruptions, which consecutively affects the other load connected to the power systems. The main purpose of this article is to present a comprehensive review of various power quality analysis techniques such as heuristic optimization, signal processing, machine learning, neural networks, artificial intelligence, and hardware implementation, so that a brief overview will be presented to the researcher and power engineers working in the field of power quality. Additionally, a comparative analysis is also reported on various methods based on several criteria including timing and accuracy, use of industrial and non-industrial data set, noisy and noiseless conditions, and finally single and multiple power quality events for analysis. More than 200 research publications are included for analysis and listed in reference so that it will be easy for the researcher in the domain of power quality to explore the possibility of further improvement in this field.

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Section: S Nomentioning

confidence: 99%

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Rahul

2020

Advcd Theory and Sims

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“…It is related to image processing; however, it can be used in a large number of different applications, since its concepts can be applied to signals of any dimensions. Dilation and erosion are elemental operations in mathematical morphology [29,40].…”

Section: Mathematical Morphologymentioning

confidence: 99%

“…In [28], a multilayer-perceptron ANN is used to correct the power factor and regulate the zero voltage in a three-phase distribution static compensator, under nonlinear loads. On the other hand, mathematical morphology is concerned with the shape of a signal waveform in the time domain [29]. Mathematical morphology has gained applications in the study of power system faults/disturbances, where the interaction with disturbances modifies the waveform information of a signal [30].…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD

et al. 2018

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Power quality disturbances (PQD) in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC)-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA)-based integrated circuit is developed to offer a portable hardware processing unit to perform fast, online PQD classification. The obtained numerical and experimental results demonstrate that the proposed method guarantees high effectiveness during online PQD detection and classification of real voltage/current signals. Author Contributions: M.L.-R. conceived the methodology, performed the experimentation and compile the mathematical definition for the treated PQD; E.C.-Y. supervised the work, defined all the tasks and wrote the paper; L.M.L.-C. provided support during experimentation and simulations; H.M.-V. provided the equipment and guided the obtaining of the real signals used for validating the proposed approach; C.R.-D. designed and implemented the data acquisition system; R.A.L.-M. verified the PC implementation for signal models and processing algorithms. Conflicts of Interest:The authors declare no conflict of interest.

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“…Park's Transformation converts vectors in the xyz reference frame to the direct-quadrature zero reference frame (0dq) [5]. The primary value of the Park transform is to rotate the reference frame of a vector at an arbitrary frequency.…”

Section: Park's Transformation Techniquementioning

confidence: 99%

Comparative study of transient voltage drop detection techniques in electric power distribution system under fault operation

Isaramongkolrak¹,

Tonmitr²,

Suksri³

2018

IJSGCE

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This paper presents a comparative study of transient voltage drop detection techniques in electric power distribution system under four types of fault operation. There are four techniques for consideration such as Fourier transformation, Park's transformation, Symmetrical component and RMS. MATLAB/Simulink blocks used for simulation with IEEE 4 bus radial distribution test system and 4 bus loop distribution test system. This paper focus on the sensitivity of each technique for detection the transient voltage drop in term of time comparison. The simulation result shown that symmetrical component technique is the best technique which spends a shorter time for detecting transient voltage drops for both of two test systems while the load bus number four was fault.

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Voltage Sag Detection Based On Dq Transform and Mathematical Morphology Filter

Cited by 14 publications

References 2 publications

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD

Comparative study of transient voltage drop detection techniques in electric power distribution system under fault operation

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