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- Title
Power quality disturbances classification based on Gramian angular summation field method and convolutional neural networks.
- Authors
Shukla, Jyoti; Panigrahi, Basanta K.; Ray, Prakash K.
- Abstract
This paper presents a novel hybrid approach combining Gramian Angular Summation Field (GASF) method with a convolutional neural network (CNN) to classify power quality disturbances. Firstly, a 1‐D Power quality disturbance signal is transformed into a 2‐D image file using GASF. Subsequently, CNN is implemented for features extraction and image classification. In this work, the synthetic power quality (PQ) disturbances are considered including nine single disturbances and five mixed disturbances. Further, to capture multi‐scale aspects of power quality disturbances problem and reduce overfitting, a unit is designed using 2‐D convolutional, pooling, and batch‐normalization layers. The classification study is further supported by experimental signals obtained on a prototype setup of PV system. The obtained results demonstrate the efficiency and reliability of the proposed method. The proposed method is compared with the other advanced CNNs and other conventional methods to illustrate its effectiveness.
- Subjects
POWER quality disturbances; CONVOLUTIONAL neural networks; PHOTOVOLTAIC power systems; FEATURE extraction
- Publication
International Transactions on Electrical Energy Systems, 2021, Vol 31, Issue 12, p1
- ISSN
2050-7038
- Publication type
Article
- DOI
10.1002/2050-7038.13222