Power quality detection and classification using digital filters

Chen, Z.; Urwin, P.

doi:10.1109/ptc.2001.964610

Cited by 15 publications

(11 citation statements)

References 3 publications

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“…In this method iterated sine window was used as a window function to extract the events' features. Kalman filters, [84][85][86][87][88] GT, [89] digital filters, [90] time-time transform, [91] curve fitting, [19] EMDRA method, [92] hybrid HT and WT with frequency shifting, [93] fractal based method, [94] covariance analysis, higher-order statistics (HOS), [95,96] change point approach, [97] and ADALINE method [98] have been proposed in the last decade. Slanted transform with FPGA hardware containing analog to digital converter AD976A, Xilinx Virtex-E XCV600E FPGA chip, and digital to analog converter AD669AN is used for PQ disturbance detection.…”

Section: S Nomentioning

confidence: 99%

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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%

“…[164] Digital filters are used for decomposition and classification of disturbances. [165] HMM based on WT, wavelet packet transform (WPT), and vector quantization is presented in refs. [33,166,167].…”

Section: Wwwadvtheorysimulcommentioning

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 can successfully detect high frequency events (transients) but in the case of slow disturbances, it performs poorly especially when the voltage variations are not sudden, but gradual. Other solutions include the combined use of wavelets and fuzzy support vector machines [18], wavelets and neural networks [19], pattern recognition [20], time-frequency representations [21] and digital filters [22].…”

Section: Introductionmentioning

confidence: 99%

Real Time PC Implementation of Power Quality Monitoring System Based on Multiharmonic Least-Squares Fitting

Ardeleanu¹,

Ramos²

2011

Metrology and Measurement Systems

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In this paper, an algorithm that monitors the power system to detect and classify power quality events in real time is presented. The algorithm is able to detect events caused by waveform distortions and variations of the RMS values of the voltage. Detection of the RMS events is done by comparing the RMS values with certain thresholds, while detection of waveform distortions is made using an algorithm based on multiharmonic leasts-squares fitting.

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“…Recommendations for monitoring of power quality (PQ) have been introduced [1,2] and a number of methods for detection and classification of individual types of disturbances that occur on the power line have been published [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Although the wavelet transform has proven to be successful in detection of many kinds of PQ disturbances, there are cases in which it cannot be used for accurate and reliable detection and subsequent classification of the PQ events. Other methods, not based on the wavelet transform, are being used for individual sets of PQ disturbances [7][8][9] but efficient detection and classification of disturbances is still an open issue.…”

Section: Introductionmentioning

confidence: 99%

On-line Detection and Classification of Power Quality Disturbances in a Single-phase Power System

Radil

Matz

Janeiro

et al. 2007

2007 International Conference on Power Engineering, Energy and Electrical Drives

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A new method for detection and classification of power quality (PQ) disturbances in single-phase power systems is presented in this paper. The method detects transients, waveform distortions and short and long duration disturbances such as sags or swells. The method was developed with special consideration for its suitability for implementation in a digital signal processor (DSP) and for real-time operation. The proposed method was implemented and its performance was verified on detection of real PQ disturbances in a PC based system monitoring a single-phase power system.

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Power quality detection and classification using digital filters

Cited by 15 publications

References 3 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

Real Time PC Implementation of Power Quality Monitoring System Based on Multiharmonic Least-Squares Fitting

On-line Detection and Classification of Power Quality Disturbances in a Single-phase Power System

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