A fuzzy-expert system for classifying power quality disturbances

Liao, Yuan; Lee, Jong-Beom

doi:10.1016/j.ijepes.2003.10.012

Cited by 110 publications

(40 citation statements)

References 1 publication

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“…Problem Specific Methodology Used [52] 2015 Power disturbance Classification SVM [53] 2013 Power disturbance Classification DT, ANN, neuro-fuzzy, SVM [54] 2014 Power disturbance Classification DT, SVM [55] 2014 Power disturbance Classification DT [56] 2012 Power disturbance Classification DT, DE [57] 2004 Power disturbance Classification Fuzzy expert, ANN [58] 2010 Power disturbance Classification Fuzzy classifiers [59] 2010 Power disturbance Classification GFS [48] 2006 Appliance load monitoring Classification ANN [60] 2009 Appliance load monitoring Classification k-NN, DTs, naive Bayes [61] 2010 Appliance load monitoring Classification k-NN, DTs, naive Bayes [62,63] 2010 Appliance load monitoring Classification LR, ANN [64] 2012 Appliance load monitoring Classification SVM [65] 2013 Solar Classification, regression SVM, ANN, ANFIS, wavelet, GA [66] 2008 Solar Classification, regression ANN, fuzzy systems, meta-heuristics [67] 2004 Solar Classification PNN [68] 2006 Solar Classification PNN [69] 2009 Solar Classification PNN, SOM, SVM [70] 2004 Solar Classification SVM [71] 2014 Solar Classification SVM [72] 2015 Solar Classification SVM [73] 2006 Solar Classification Fuzzy rules [74] 2013 Solar Classification Fuzzy classifiers [75] 2014 Solar Classification Fuzzy rules…”

Section: Ref Year Application Fieldmentioning

confidence: 99%

“…The results in terms of accuracy indicate that the use of the clustering approach improves the performance of the fuzzy DT on its own. In [57], a fuzzy expert system is applied for a classification problem of PQ events. Different novel predictive variables are introduced based on a wavelet transform of the power signal.…”

Section: Classification Problems and Algorithms In Power Quality Distmentioning

confidence: 99%

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A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

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Section: Ref Year Application Fieldmentioning

confidence: 99%

Section: Classification Problems and Algorithms In Power Quality Distmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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“…At present, the research on PQ early warning systems is still minimal and mainly concentrates on analyzing the PQ characteristics of different disturbance sources and the detection and identification of disturbance signals [6][7][8][9]. Conversely, the research on PQ evaluation is relatively mature [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Early Warning Based on Anomaly Detection

Gu¹,

Bai²,

Yuan³

et al. 2014

Journal of Electrical Engineering and Technology

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-Different power quality (PQ) disturbance sources can have major impacts on the power supply grid. This study proposes, for the first time, an early warning approach to identifying PQ problems and providing early warning prompts based on the monitored data of PQ disturbance sources. To establish a steady-state power quality early warning index system, the characteristics of PQ disturbance sources are analyzed and summed up. The higher order statistics anomaly detection (HOSAD) algorithm, based on skewness and kurtosis, and hierarchical power quality early warning flow, were then used to mine limit-exceeding and abnormal data and analyze their severity. Cases studies show that the proposed approach is effective and feasible, and that it is possible to provide timely power quality early warnings for limit-exceeding and abnormal data.

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“…The usual approach is primarily based on the transformation and reconstruction of the original waveform, extracting classification features, impelling a large number of signal processing methods applied to the detection of power quality problems. The most commonly used feature extraction method is Wavelet Transform [2][3][4][5][6][7] . Fourier Transform [4] , dq Transform [8] , Stransform [9][10][11][12] , Walsh Transform [11] and HHT [13] were also used for disturbance features extraction.…”

Section: Introductionmentioning

confidence: 99%

“…The most commonly used feature extraction method is Wavelet Transform [2][3][4][5][6][7] . Fourier Transform [4] , dq Transform [8] , Stransform [9][10][11][12] , Walsh Transform [11] and HHT [13] were also used for disturbance features extraction.…”

Section: Introductionmentioning

confidence: 99%

Classification of Composite Power Quality Disturbance Signals Based on HHT and S-Transform

Yu¹,

Li²,

Li³

et al. 2013

Proceedings of the 3rd International Conference on Electric and Electronics

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Abstract-The correct classification of single and composite power quality disturbances is the premise and basis of governance and control of power quality problems. This article extracts the high-frequency and low-frequency characteristics by S-transform of the disturbance signals, combining HHT to extract the instantaneous amplitude of the signal before and after the disturbance. Some characteristic functions is defined as the classification criterions by analyzing the characteristics of the disturbance signals, as to achieve the classification of power quality disturbances. Experimental results show that the fusion of the two methods can accurately detect the categories and characteristics of power quality disturbance signals, the starting and ending time of transient disturbance signals.

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A fuzzy-expert system for classifying power quality disturbances

Cited by 110 publications

References 1 publication

A Review of Classification Problems and Algorithms in Renewable Energy Applications

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Power Quality Early Warning Based on Anomaly Detection

Classification of Composite Power Quality Disturbance Signals Based on HHT and S-Transform

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