An Algorithm Based on Hilbert Transform and Rule Based Decision Tree Classification of Power Quality Disturbances

Saini, Rahul; Mahela, Om Prakash; Sharma, Deepak

doi:10.1109/poweri.2018.8704465

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…This technique has the average efficiency of 98.20% and 97.33% for recognition of single-stage and multiple PQ disturbances, respectively. The algorithm proposed in this paper has combined the Stockwell transform and Hilbert transform to improve performance of the PQ recognition, and average efficiency of 99.625% is achieved which is higher compared to the efficiency of algorithms reported in [12], [13], [14] and [15]. These papers have been considered for comparative study because waveforms of PQDs investigated in these papers are similar to that considered in this paper.…”

Section: Performance Comparative Studymentioning

confidence: 95%

“…This technique has the average efficiency of 97.033% and 96.67% for recognition of single-stage and multiple PQ disturbances, respectively. Further, a technique using variance features extracted using HT decomposition of voltage signal with single-stage PQ and multiple PQ is reported in [14] and [15], respectively, where the classification of PQDs is achieved using the RBDT. This technique has the average efficiency of 98.20% and 97.33% for recognition of single-stage and multiple PQ disturbances, respectively.…”

Section: Performance Comparative Studymentioning

confidence: 99%

“…However, this method is less effective to identify the PQDs related to amplitude. A technique using HT based processing of voltage signal with both single-stage and multiple PQDs is reported in [14] and [15] respectively, where the classification of the PQDs is achieved using rule based decision tree (RBDT). This technique is effective for identifying the amplitude-related PQDs such as sag, swell, MI, MS and MN.…”

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confidence: 99%

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A Hybrid Algorithm for Recognition of Power Quality Disturbances

Kaushik

Mahela²,

Bhatt

et al. 2020

IEEE Access

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An algorithm making use of hybrid features of Hilbert transform (HT) and Stockwell transform (ST) to identify the single-stage and multiple (multi-stage) power quality disturbances (PQDs) is introduced in this manuscript. A power quality index (PI) and time location index (TLI), based on the features computed from the voltage signal by the use of HT and ST are proposed for recognition of the PQDs. Four features extracted from the PI and TLI are considered for classification of the PQDs achieved using decision tree driven by rules. The algorithm is tested on the PQDs generated with the help of mathematical models (in conformity with standard IEEE-1159). Performance is evaluated on 100 data set of every disturbance computed by varying various parameters, and efficiency is found to be greater than 99%. It is established that an algorithm is effective for recognition of PQ events with an efficiency greater than 98% even in the presence of high-level noise. Algorithm is faster compared to many reported techniques and scalable for application to voltages of all range. Results are validated through comparison with the results of the algorithms reported in the literature. Performance of the algorithm is effectively validated on the practical utility network. This algorithm can be effectively implemented for designing the power quality (PQ) monitoring devices for the utility grids.INDEX TERMS Hilbert transform, rule based decision tree, power quality disturbance, power quality index, Stockwell transform, time location index.

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Section: Performance Comparative Studymentioning

confidence: 95%

Section: Performance Comparative Studymentioning

confidence: 99%

mentioning

confidence: 99%

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A Hybrid Algorithm for Recognition of Power Quality Disturbances

Kaushik

Mahela²,

Bhatt

et al. 2020

IEEE Access

Self Cite

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“…This makes it very attractive for real-time deployment. DT has been successfully applied to PQ event classification problem in [16] with Hilbert Transform. The total data-set consists of 678 3phase voltage snapshot.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

Advanced Edge Computing Framework for Grid Power Quality Monitoring of Industrial Motor Drive Applications

Bhoi

Chakraborty

Verbrugge

et al. 2022

2022 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)

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“…Corresponding features represent the different disturbance signals and construct the feature matrix. Then, the machine learning classification algorithm [10][11][12] is used for the extracted signal features to realize the classification and identification of disturbance signals. This method based on manual feature extraction has a complicated calculation process and difficult parameter selection.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Power Quality Disturbance Based on RCNN

Fan

Xiao

et al. 2022

J. Phys.: Conf. Ser.

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With the development of the smart grid, the problem of power quality disturbance is becoming more and more serious. To improve the classification ability of power quality disturbance signals, this paper designs a recurrent convolutional neural network model to realize the classification and identification of power quality disturbance signals. The model first uses the bidirectional long short-term memory neural network module to encode the disturbance signal. Then it uses the convolutional layer to perform spatial feature extraction and enforces the disturbance classification through the fully connected layer. The model is verified and analyzed by simulation data, which shows that the model has both good generalization ability and higher accuracy.

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An Algorithm Based on Hilbert Transform and Rule Based Decision Tree Classification of Power Quality Disturbances

Cited by 8 publications

References 7 publications

A Hybrid Algorithm for Recognition of Power Quality Disturbances

A Hybrid Algorithm for Recognition of Power Quality Disturbances

Advanced Edge Computing Framework for Grid Power Quality Monitoring of Industrial Motor Drive Applications

Recognition of Power Quality Disturbance Based on RCNN

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