A packet-loss resilient protection scheme for hybrid microgrids based on Markov chain model and spline interpolation

Rameshrao, Awagan Goyal; Koley, Ebha; Ghosh, Subhojit

doi:10.1016/j.segan.2023.101121

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) 𝜇 = ( ∑ 6000 𝑖 = 1 𝑥 𝑖 )/6000 for detecting and correcting outliers. In fact, there are many algorithms to correct outliers, such as multiple imputation 10) , spline interpolation 11) , etc. However, in the application, the database has a high data transfer rate, which requires the computational complexity of the algorithm to be as small as possible to meet the low latency of output results.…”

Section: Detection and Correction Of Outliersmentioning

confidence: 99%

Dynamic Prediction Model of Yield of Molten Iron Based on Multi-Head Attention Mechanism

Duan,

Liu,

et al. 2024

ISIJ Int.

View full text Add to dashboard Cite

：Efficient matching of the preheat-numbers of the molten iron ladle with yield of molten iron in a single iron time is conducive to reducing energy waste, so it is of great economic significance to iron and steel enterprises to accurately predict yield of molten iron according to the characteristics of the per furnace. In this article, considering the variation of yield of molten iron and corresponding synergistic parameters depending on furnace conditions, we use the multi-head attention mechanism to capture the Attention-score of the feature parameters, and the weight matrix of the predictor is dynamically adjusted, so that it can assign more training weights to high-value feature parameters in real time according to the change of furnace conditions, efficiently complete the prediction of yield of molten iron. First, twenty characteristics are selected by mutual information method. Then, the influence degree of selected features on yield of molten iron is calculated in real time. Finally, stacked denoising auto encoder (SDAE) is used to train the deep neural network, and we constructed the predictor after adjusting the parameters to optimal. The test results show that the prediction accuracy of proposed model is 95% under the error range of ±50t, which is higher than traditional SVM, DAE, and SDAE model without the multi-head attention mechanism. Ultimately, we analyze and quantify the influence process of 20 factors on yield of molten iron, and develop a dynamic prediction system of yield of molten iron based on proposed model, which has good applicability for the prediction of yield of molten iron and effectively guides the transfer scheduling of molten iron ladles.Keywords：Dynamic prediction model of yield of molten iron; Multi-head attention mechanism; Stacked denoising auto encoder; Transfer scheduling of molten iron ladles;

show abstract

Section: Detection and Correction Of Outliersmentioning

confidence: 99%