Automatic pattern recognition of single and multiple power quality disturbances

Khokhar, Suhail; Zin, Abdullah Asuhaimi Mohd; Mokhtar, Ahmad Safawi; Zareen, Naila

doi:10.1080/1448837x.2015.1092932

Cited by 12 publications

(11 citation statements)

References 28 publications

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“…Based on the mathematical models of PQDs, MATLAB software was employed to generate the PQ disturbances of the 31 categories of single and multiple problems in a power network, by changing the specified bounds of the PQD mathematical models. A total of 10 cycles were considered for each PQD, which contains total 5120 points/10 cycle of the synthetic dataset, whereas the fundamental frequencies of each PQDs is 50 Hz.…”

Section: Methodsmentioning

confidence: 99%

“…In, however, six kinds of combined PQD waveforms were classified by rank wavelet and SVM. In Liu et al, an automatic classification algorithm was presented for classification of single and multiple PQD based on wavelet norm entropy features and probabilistic neural network (PNN). Decision tree‐based method was proposed for the classification of multiple and single PQD .…”

Section: Introductionmentioning

confidence: 99%

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Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine‐tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS‐ and SAE‐based DL algorithms can be efficiently used for single and multiple PQDs classifications.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Int Trans Electr Energ Syst

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“…It is common to find in the literature PQD signal models based on mathematical equations [2], [12], [13] and [14]. Although real-time PQ disturbances signals are difficult to capture [12], synthetically-obtained waveforms can be an interesting alternative.…”

Section: Pqd Parameter-controlled Equationsmentioning

confidence: 99%

PQD classifier based on higher-order statistics and total harmonic distortion

Gonzalez-Bueno¹,

Palomares-Salas²,

González-de-la-Rosa³

et al. 2019

REPQJ

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Higher-Order Statistics (HOS) have been frequently applied in Power Quality Disturbance (PQD) analysis as a reliable tool for event detection. This paper outlines a technique based on mean, variance and zero-lag third and fourth cumulants-skewness and kurtosis-along with the Total Harmonic Distortion (THD) index for PQD detection. These statistics are obtained in order to characterize a waveform by a feature vector. A two-layer feed-forward neural network is then used to classify inputs (feature vectors) into a set of PQD categories. The impact of frame duration and number of hidden neurons is analyzed. The network is trained, validated and tested with synthetically-generated PQD waveforms obtained from parameter-controlled equations. As a first approach, five PQD categories are considered: sag, swell, interruption, impulsive transient and oscillatory transient. A promising overall classification rate of 99.7 % is achieved which allows future analysis with more PQD categories and/or a noisy context.

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“…PQDs are broadly categorized as, magnitude variation, transients and steady state variations or harmonics. Magnitude variations are classified as the voltage/current sag (0.1-0.9 P.U), swell (1.1-1.8 P.U), interruption (<0.1 P.U) [4], while transients can be further classified as impulsive and oscillatory transients. Consequently, steady-state PQDs are typically classed into harmonics, flicker and notch categories.…”

Section: Introductionmentioning

confidence: 99%

“…[3,9,10]. Although support vector machine classifier has a lot of tuning parameters but it is suitable because of its high classification accuracy, robustness, and less computational time requirements [4,11].…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification of Power Quality Disturbances by DWT-MRA and SVM Classifier

Jandan¹,

Khokhar²,

Shaha³

et al. 2019

ijacsa

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Electrical power system is a large and complex network, where power quality disturbances (PQDs) must be monitored, analyzed and mitigated continuously in order to preserve and to re-establish the normal power supply without even slight interruption. Practically huge disturbance data is difficult to manage and requires the higher level of accuracy and time for the analysis and monitoring. Thus automatic and intelligent algorithm based methodologies are in practice for the detection, recognition and classification of power quality events. This approach may help to take preventive measures against abnormal operations and moreover, sudden fluctuations in supply can be handled accordingly. Disturbance types, causes, proper and appropriate extraction of features in single and multiple disturbances, classification model type and classifier performance, are still the main concerns and challenges. In this paper, an attempt has been made to present a different approach for recognition of PQDs with the synthetic model based generated disturbances, which are frequent in power system operations, and the proposed unique feature vector. Disturbances are generated in Matlab workspace environment whereas distinctive features of events are extracted through discrete wavelet transform (DWT) technique. Machine learning based Support vector machine classifier tool is implemented for the classification and recognition of disturbances. In relation to the results, the proposed methodology recognizes the PQDs with high accuracy, sensitivity and specificity. This study illustrates that the proposed approach is valid, efficient and applicable.

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Automatic pattern recognition of single and multiple power quality disturbances

Cited by 12 publications

References 28 publications

Classification of multiple power quality events via compressed deep learning

Classification of multiple power quality events via compressed deep learning

PQD classifier based on higher-order statistics and total harmonic distortion

Recognition and Classification of Power Quality Disturbances by DWT-MRA and SVM Classifier

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