Combined classification of Power Quality Disturbances and Power System Faults

Shaik, Aslam; Reddy, A. Srinivasula

doi:10.1109/iceeot.2016.7755423

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shaik et al. [43] contracts with the introduction of an intelligent classifier with the comparison of the wave patterns of different PQD and the faults in power system. In this method, ANN acts as the classifier and wavelet is considered as the strategy to categorize the problems in the quality of power.…”

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

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%