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"A Deep Neural Network Based Robust Intelligent Strategy for Microgrid " by Erphan A. Bhuiyan, Shahriar Rahman Fahim et al.

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 Commission , White Gaussian Noise , Deep Belief Network , Discrete Wavelet Transform , Fault Diagnosis ,

"New Method for Battery Sizing in Microgrids by Seeing Battery Autonomy" by Fareeha Anwar, Asad Waqar et al.

In this paper, the authors have introduced a two-step method using MILP to size batteries along with DERs in a microgrid. The optimal objectives are to minimize simulation time and total net present cost (TNPC) of the microgrid at minimum unmet deferrable load (UDL) and optimally sizing batteries along with DERs. Therefore, battery size is selected as an optimal variable. It is achieved by seeing battery autonomy (BA) as a chance constraint. In the first-step, the simulations are performed based on random input vectors of batteries, converters (PCS), and DERs, including diesel generators (DGENS) and PV, and results are generated. Then chance constraint is applied to BA, and respective probability indices are calculated. Based on the threshold of probability index of BA, minimized objectives are calculated subject to constraints and presented as a Pareto front of TNPC vs. UDL. In the second-step, based on the highest probability index of BA, another battery-sizes vector is estimated, an ....

Battery Autonomy , Areto Fronts ,