Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements

Tuo, Mingjian; Li, Xingpeng

doi:10.1109/ias54023.2022.9940112

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…Jiang et al [24] discussed energy management in stand-alone and grid-connected modes and provided strategic insights on the best possible power flow. A stochastic-predictive energy management system for isolated microgrids was proposed by Olivares et al [25][26][27] in order to handle the uncertainties related to renewable energy sources.…”

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confidence: 99%

Optimal Power Flow in Hybrid AC/DC Microgrid using ANN for Cost Minimization

Yamuna,

2024

JMC

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Currently, this work lays the ground work for sophisticated control methods and decision support systems in hybrid microgrid operations by providing insightful information about integrating artificial intelligence for improved microgrid control. In this work, a neural network (NN) method is proposed for power flow analysis in an IEEE 12-bus-based Hybrid AC/DC Microgrid. The study optimizes power dispatch, minimizes expenses, and minimizes losses in both AC and DC components. Simulation is carried using MATLAB software and the results are presented and analysed. The accuracy of the NN’s predictions of active power flows is demonstrated by training it on historical data and validating it on real-time observations. Regression plots comparing anticipated and real values demonstrate the effectiveness of NN-based analysis in reaching the ideal power distribution.

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confidence: 99%