Classification of power system disturbances using linear Kalman filter and fuzzy-expert system

Abdelsalam, Abdelazeem A.; ElDesouky, Azza A.; Sallam, Abdelhay A.

doi:10.1016/j.ijepes.2012.05.052

Cited by 76 publications

(38 citation statements)

References 27 publications

(31 reference statements)

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“…On the other hand, there are few methodologies that consider time-frequency analysis allowing the detection and classification of two or more PQD [12][13][14][15][16][17][18][19][20][21][22]. For instance, in [23], a research on voltage fluctuation and flicker measurement based on fast Fourier transform (FFT), is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm is the hybrid combination of Daubechies wavelets of order 2 and order 8. An approach based on the combination of WT, covariance, linear Kalman filter, voltage signal features, and a fuzzy expert system is proposed in [22] for identifying and classifying interruptions, sags, and swells. Most of the approaches in the reviewed literature apply combined analyses to detect PQD utilizing different techniques.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…Another research by Abdelsalam et al [4] discussed on power quality identification and classification using linear Kalman filter with discrete wavelet transform (DWT) which could not identify the fault signature.…”

Section: Fault Signaturementioning

confidence: 99%

Protection system analysis using fault signatures in Malaysia

Zin

Karim

2013

International Journal of Electrical Power & Energy Systems

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“…For this purpose, such signal processing approaches as Fourier transformation [9,10], wavelet transformation (WT) [11,12], Kalman filtering [13,14] and -transformation [15,16] are commonly used. The least squares fitting is one of the commonly used methods to find the fitting function, and it is widely used in practical engineering [17].…”

Section: Introductionmentioning

confidence: 99%

Power quality prediction based on least squares method

Lu¹

2017

Vib. proced.

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Abstract. In the current high degree of popularity of power products, power abnormalities on the production of more and more. Therefore, the prediction of power quality is of great significance. The method of prediction is generally to find the fitting function, the least squares fitting is the commonly used method to find the fitting function. In this paper, the least square method is used to fit the data of power grid. The factors influencing power quality were analyzed from four aspects, and the conclusions were obtained by fitting with real data points. In order to further improve the power quality prediction.

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Classification of power system disturbances using linear Kalman filter and fuzzy-expert system

Cited by 76 publications

References 27 publications

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

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

Protection system analysis using fault signatures in Malaysia

Power quality prediction based on least squares method

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