Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier

Aker, Elhadi; Othman, Mohammad Lutfi; Veerasamy, Veerapandiyan; Aris, Ishak; Wahab, Noor Izzri Abdul; Hizam, Hashim

doi:10.3390/en13010243

Cited by 56 publications

(27 citation statements)

References 32 publications

(43 reference statements)

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“…In this work, we used the DWT for extracting the signal characteristics due mainly to its flexible time-scale and high conservation of information without reduced resolution [17,48]. This method has also been used extensively in many applications for power systems, such as fault detection and localization, classification of the shunt compensated transmission line, islanding detection technique, and PQD classification [48][49][50][51]. The DWT is transformed from the WT based on a discrete wavelet scales.…”

Section: Wavelet Transformmentioning

confidence: 99%

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

et al. 2021

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Power quality disturbance (PQD) is an influential situation that significantly declines the reliability of electrical distribution systems. Therefore, PQD classification is an important process for preventing system reliability degradation. This paper introduces a novel algorithm called “adaptive salp swarm algorithm (SSA)” as an optimal feature selection algorithm for PQD classification. Feature extraction and classifier of the proposed classification system were based on the discrete wavelet and the probabilistic neural network, respectively. The classification was focused on the 13 types of power quality signals. The optimal number of selected features for the proposed classification system was firstly determined. Then, it demonstrated that the optimally selected features resulted in the highest classification accuracy of 98.77%. High performance of the proposed classification system in the noisy environment, as well as based on the real dataset was also verified. Furthermore, the proposed SSA indicates a very high convergence rate compared to other well-known algorithms. A comparison of the proposed classification system’s performance to existing works was also carried out, revealing that the proposed system’s accuracy is on a high-range scale. Hence, the adaptive SSA becomes another efficient optimal feature selection algorithm for PQD classification.

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Section: Wavelet Transformmentioning

confidence: 99%

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

et al. 2021

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“…In the context of developing intelligent frameworks for PdM purposes, the utilisation of supervised machine learning algorithms such as the Artificial Neural Networks (ANNs) [21][22][23][24], Support Vector Machine (SVM) [25][26][27][28], Linear Discriminant Analysis (LDA) [29][30][31], and the Bayes classifiers [32][33][34][35] have been well studied. Although these algorithms have yielded to some extent satisfactory results, they are prone to local optima entrapment when their required hyperparameters are not appropriately fine-tuned.…”

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification

Buabeng

Simons

Frempong

et al. 2022

Mathematical Problems in Engineering

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With the rising demand for integrated and autonomous systems in the field of engineering, efficient frameworks for instant detection of performance anomalies are imperative for improved productivity and cost-effectiveness. This study proposes a systematic predictive maintenance framework based on the hybrid multisensor fusion technique of fuzzy rough set feature selection and stacked ensemble for the efficient classification of fault conditions characterised by uncertainties. First, a feature vector of time-domain features was extracted from 17 multiple sensor signals. Then, a comparative study of six different Fuzzy Rough Set Feature Selection (FRFS) methods was employed to select the various combinations of optimal feature subsets for various faults classification tasks. The determined optimal feature subsets then served as inputs for training the stacked ensemble (ESB(STK)). In the ESB(STK), Support Vector Machine (SVM), Multilayer Perceptron (MLP), k -Nearest Neighbour ( k -NN), C4.5 Decision Tree (C4.5 DT), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) served as the base classifiers while the LR was selected to be the metaclassifier. The proposed hybrid framework (FRFS-ESB(STK)) improved the classification accuracy with the selected combinations of optimal feature subset size whiles reducing the computational cost, overfitting, training runtime, and uncertainty in modelling. Overall analyses showed that the FRFS-ESB(STK) proved to be generalisable and versatile in the classification of all conditions of four monitored hydraulic components (i.e., cooler, valve, accumulator, and internal pump leakage) when compared with the six base classifiers (standalone) and three existing ensemble classifiers (Stochastic Gradient Boosting (SGB), Ada Boost (ADB), and Bagging (BAG)). The proposed FRFS-ESB(STK) showed an average improvement of 11.28% and 0.88% test accuracies when classifying accumulator and pump conditions, respectively, whiles 100% classification rates were obtained for both cooler and valve.

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“…With the popularization and application of smart substations and distribution automation equipment, the value of massive power data has been widely considered. Artificial intelligence technology has been widely used in single-phase-to-ground faults detection because of its efficient data processing capabilities, learning efficiency, and superior performance [13][14][15][16][17]. In [13], a method of timeseries imaging using three-phase current and three-phase voltage is proposed, and the image is used as the input of the self-attention convolutional neural network for fault identification.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], a fault feeder identification method based on discrete wavelet transform and Bayesian selectivity technique is proposed. In [17], an approach of decomposing the three-phase fault current waveform by discrete wavelet transform, extracting features such as standard deviation and energy value, and then using a naive Bayes classifier for fault identification is proposed.…”

Section: Introductionmentioning

confidence: 99%

Fault Line Selection Method Based on Transfer Learning Depthwise Separable Convolutional Neural Network

Zhang

Cheng²

2021

Journal of Electrical and Computer Engineering

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With the continuous development of artificial intelligence technology, the value of massive power data has been widely considered. Aiming at the problem of single-phase-to-ground fault line selection in resonant grounding system, a fault line selection method based on transfer learning depthwise separable convolutional neural network (DSCNN) is proposed. The proposed method uses two pixel-level image fusions to transform the three-phase current of each feeder into the RGB color image, which is used as the input of DSCNN. After DSCNN self-feature extraction, the fault line selection is completed. With the consideration that not all of power distribution systems can obtain a large amount of data in practical applications, the transfer learning strategy is adopted to transplant the trained line selection model. The smaller number of DSCNN parameters increases the portability of the model. The test results show that not only does the proposed method extracts obvious features, but also the line selection accuracy can reach 99.76%. It also has good adaptability under different sampling frequencies, different noise environments, and different distribution network topologies; the line selection accuracy can reach more than 97.43%.

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Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier

Cited by 56 publications

References 32 publications

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

Predictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification

Fault Line Selection Method Based on Transfer Learning Depthwise Separable Convolutional Neural Network

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