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A Deep Neural Network Based Robust Intelligent Strategy for Microgrid Fault Diagnosis by Erphan A. Bhuiyan; Shahriar Rahman Fahim; Subrata K. Sarker; Sajal K. Das; Md. Rabiul Islam; Kashem Muttaqi is a scholarly article available to read on EtoBox.

What is A Deep Neural Network Based Robust Intelligent Strategy for Microgrid Fault Diagnosis about?

Microgrids frequently experience a massive amount of faults, which compromise stable operation, disrupts the loads, and increases the grid recovery expenditures. The diagnosis of microgrid system faults is severely reliant on dimensionality reduction and requires complex data acquisition. To address these issues, machine learning-based methods are extensively implemented for fault diagnosis of microgrids providing robust features and handling a massive amount of data. However, the existing machine learning techniques use simplified models which are not capable of investigating diverse and implicit features and also are time-intensive. In this paper, a novel method based on a multiblock deep belief network (DBN) is suggested for fault diagnosis, underlying discrete wavelet transform (DWT), which allows the framework to discover the probabilistic reconstruction across its inputs. This approach equips a robust hierarchical generative model for exploiting features associated with faults, interprets highly variable functions, and needs lesser prior information. Moreover, the method instantaneously categorizes the fault modes, which eventually strengthens the adaptability of applying it

Author

Erphan A. Bhuiyan; Shahriar Rahman Fahim; Subrata K. Sarker; Sajal K. Das; Md. Rabiul Islam; Kashem Muttaqi

Publisher

IEEE

Published

2021

Language

EN