Dual tree complex wavelet transform based approach for power quality monitoring and data compression

Manjunatha, A.; Basavaraj, S.

doi:10.1109/pestse.2016.7516521

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Manjunatha and Sunil Basavaraj [25] proposed Dual Tree Complex Wavelet Transform (DTCWT) combined with run length encoding for power quality monitoring. The strategy addresses transients, voltage swell, sag, and flickers.…”

Section: Literature Reviewmentioning

confidence: 99%

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Kandasamy,

Kumar,

Lakshmanan

et al. 2023

IET Generation Trans & Dist

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The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Kandasamy,

Kumar,

Lakshmanan

et al. 2023

IET Generation Trans & Dist

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“…Cheng [ 28 ] proposed a method for compressing oscillation wide-area measurement data based on wavelet transform, which selects the wavelet function and decomposition scale according to the oscillation frequency of the power system in the wide-area measurement system. Prathibha [ 29 ] proposed a dual-tree complex wavelet transform method for power quality monitoring and combined with run-length coding technology to compress the disturbing data. Ruiz [ 30 ] used a six-level biorthogonal wavelet transform method to improve the compression rate of compressed power quality signals.…”

Section: Related Workmentioning

confidence: 99%

Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data

Zhu

et al. 2022

Sensors

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Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.

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Study on weak Feature Extraction of The complex PQ Disturbance based on Multi-level SVD

Liu¹,

Tang²

2019

2019 Chinese Automation Congress (CAC)

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Dual tree complex wavelet transform based approach for power quality monitoring and data compression

Cited by 4 publications

References 7 publications

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data

Study on weak Feature Extraction of The complex PQ Disturbance based on Multi-level SVD

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