A Review of Uncertainties in Power Systems—Modeling, Impact, and Mitigation

Hu, Hongji; Yu, Samson S.; Trinh, Hieu

doi:10.3390/designs8010010

Cited by 1 publication

(1 citation statement)

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“…These conditions are also critical for the control of the grid operator that must prioritize wind turbine substations, improve the entrance of electrical power into the grid, and foresee future energy requirements and issues [5,6]. Two options are used in various studies to preserve the utility grid's performance indices within permitted limitations, depending on changes in the climate and violent interruptions of RESs in grid-connected or islanded modes [7].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms

Sharkawy,

Ameen,

Mohamed

et al. 2024

Automation

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The use of renewable energy, especially wind power, is the most practical way to mitigate the environmental effects that various countries around the world are suffering from. To meet the growing need for electricity, wind energy is, nevertheless, being used more and more. Researchers have come to understand that a near-perfect output power estimate must be sacrificed. Variations in the weather influence wind energy, including wind speed, surface temperature, and pressure. In this study, the wind turbine output power was estimated using three approaches of artificial neural networks (ANNs). The multilayer feed-forward neural network (MLFFNN), cascaded forward neural network (CFNN), and recurrent neural network (RNN) were employed for estimating the entire output power of wind turbine farms in Egypt. Therefore, each built NN made use of wind speed, surface temperature, and pressure as inputs, while the wind turbine’s output power served as its output. The data of 62 days were gathered from wind turbine farm for the training and efficiency examination techniques of every implemented ANN. The first 50 days’ worth of data were utilized to train the three created NNs, and the last 12 days’ worth of data were employed to assess the efficiency and generalization capacity of the trained NNs. The outcomes showed that the trained NNs were operating successfully and effectively estimated power. When analyzed alongside the other NNs, the RNN produced the best main square error (MSE) of 0.00012638, while the CFNN had the worst MSE of 0.00050805. A comparison between the other relevant research studies and our suggested approach was created. This comparison led us to the conclusion that the recommended method was simpler and had a lower MSE than the others. Additionally, the generalization ability was assessed and validated using the approved methodology.

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Section: Introduction 1backgroundmentioning

confidence: 99%