Intelligent Flexible Priority List for Reconfiguration of Microgrid Demands Using Deep Neural Network

Alahmed, Ahmed S.; Salman, Umar T.; Abido, M. A.; Almuhaini, Mohammed M.

doi:10.1109/isgt-asia.2019.8881363

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…There are some parameters that can be adjusted to increase the learning performance such as the number of epochs that can be selected by default provided by NN toolbox. While others such as the number of neurons and size of hidden layers can be chosen by trial and error experience 35 . The error and average error to evaluate the effectiveness of the model are computed using ( 1) and ( 2…”

Section: Nn For Corrosion Modelingmentioning

confidence: 99%

Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data

Memon

Imran

Alhems

2023

Preprint

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Stainless steel (SS) is used in many industrial applications that demand superior corrosion resistance. Modeling its corrosion behavior in common structural and various operational scenarios is beneficial to provide wall-thickness (WT) information, thus leading to a predictive asset integrity regime. In this spirit, modeling of SS 316L corrosion behavior using artificial neural networks (ANNs) is presented, whereby saline water at different concentrations is flown through an elbow structure at different flow rates and salt concentrations. Voltage, current, and temperature data are recorded hourly using electric field mapping (EFM) pins installed on the elbow surface and used for training the model. The performance of corrosion modeling is verified by comparing the predicted WT with actual measurements obtained from experimental tests. Moreover, a concise account of the observed scale formation is also reported.‏

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Section: Nn For Corrosion Modelingmentioning

confidence: 99%

Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data

Memon

Imran

Alhems

2023

Preprint

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“…Salman et al [145], proposed the artificial neural network (ANN) for the restoration of different load categories after a fault is cleared. This study adopts a smart and dynamic prioritization of different load categories, such as commercial, residential, hospital and industrial, based on the load importance, available power and reliability at a particular time.…”

Section: Flexibility Based On Energy Forecastingmentioning

confidence: 99%

A Review of Improvements in Power System Flexibility: Implementation, Operation and Economics

et al. 2022

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This study presents a literature review on the concept of power system flexibility in terms of its definition, indices, algorithms, implementation, economic impacts, operational impacts, and security. Although there are tremendous reviews on this subject in the literature, each paper discusses specific aspects of flexibility. Moreover, the literature is devoid of a comprehensive review of the latest improvements in terms of implementation, operation, and economics, which are addressed by the collections presented in this study. This paper, therefore, surveys some improvements that have been made in recent decades. Furthermore, we highlight the impact of the high penetration of renewable energy and energy storage systems towards enhancing the improvement of power system flexibility.

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“…ANN has network architecture consisting of neurons, connecting strength, nodes properties, and updating rules [30,31]. The neurons have natural ability to store and figure out experimental knowledge which can be used to validate future occurrences [32].…”

Section: Network Trainingmentioning

confidence: 99%

Short term prediction of wind speed based on long-short term memory networks

Salman¹,

Rehman²,

Alawode³

et al. 2021

FME Transactions

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Power utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge of wind speed both in spatial and time domains is critical. The wind speed is the most fluctuating and intermittent parameter in nature compared to all the meteorological variables. This uncertain nature of wind speed makes wind power more difficult to predict ahead of time. Wind speed is dependent on meteorological factors such as pressure, temperature, and relative humidity and can be predicted using these meteorological parameters. The forecasting of wind speed is critical for grid management, cost of energy, and quality power supply. This study proposes a short-term, multi-dimensional prediction of wind speed based on Long-Short Term Memory Networks (LSTM). Five models are developed by training the networks with measured hourly mean wind speed values from1980 to 2019 including exogenous inputs (temperature and pressure). The study found that LSTM is a powerful tool for a short-term prediction of wind speed. However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of prediction ahead.

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Intelligent Flexible Priority List for Reconfiguration of Microgrid Demands Using Deep Neural Network

Cited by 10 publications

References 11 publications

Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data

Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data

A Review of Improvements in Power System Flexibility: Implementation, Operation and Economics

Short term prediction of wind speed based on long-short term memory networks

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