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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, i

International electrotechnical commissionWhite gaussian noiseDeep belief networkDiscrete wavelet transformFault diagnosis