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access icon free Deep-learning based fault diagnosis using computer-visualised power flow

© The Institution of Engineering and Technology
Received 08/04/2018, Accepted 02/07/2018, Revised 14/06/2018, Published 01/08/2018

Changes in system topology, such as branch breaking and the loss of a generator or load, may profoundly influence the operation security of the power system. This study introduces a novel deep-learning based fault diagnosis method using power flow to diagnose topology changes in the power system. Power flow samples with different system states and topologies are first computed numerically; then, they are transformed into computer-visualised images. Using massive power-flow image samples, a convolutional neural network that aims to identify the system state is trained. A feature-map restriction technique is used to restructure the network. To enhance the robustness of the network, the random noise of branch flow is considered in the sample generation process. The results show that the proposed deep-learning based method may diagnose system faults effectively.

Inspec keywords: neural nets; load flow; fault diagnosis; learning (artificial intelligence)

Other keywords: branch flow; operation security; power system; branch breaking; topology changes; power flow samples; computer-visualised power flow; topologies; convolutional neural network; system faults; deep-learning based method; different system states; deep-learning based fault diagnosis method; massive power-flow image samples; computer-visualised images; system topology; system state; generator; sample generation process

Subjects: Other topics in statistics; Neural computing techniques; Optical, image and video signal processing; Computer vision and image processing techniques; Knowledge engineering techniques; Other topics in statistics

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