ATC calculation including wind: A probabilistic study and a comparison of MCS and LHS

Gupta, Abhishek Kumar; Kumar, Ashwani

doi:10.1109/icpes.2016.7584171

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Some researchers employed Monte Carlo Simulation (MCS), some employed analytical approaches, while an approximate method which is a combination of numerical and analytic approaches was employed in Wei et al for ATC computation. Few works have considered the incorporation of wind energy sources in ATC evaluation. Of all the aforementioned probabilistic approaches, MCS has proven to be more efficient than other techniques due to its ability to incorporate more uncertainty parameters and system constraints.…”

Section: Uncertainties In Atc Computationsmentioning

confidence: 99%

“…Self‐adaptive algorithm incorporating the system TRM can be implemented for accurate online ATC display on OASIS. In practical scenario, there may be power wheeling on the third‐party owned lines to avoid congestions on the direct lines between the market participants. The two critical determining factors of the wheeling rates are the prices associated with the real‐time scenario and market‐based competition; hence, effective economic analysis of the ATC between the concerned areas should be incorporated in ATC computations. Few works have considered the incorporation of wind energy sources in ATC evaluation using probabilistic approach. More works should consider the uncertainties associated with other stochastic renewable energy sources in ATC assessment as expansion plans in renewables continue. MCS has been proven to be an efficient technique for probabilistic ATC evaluation.…”

Section: Future Directions and Suggestions On Atc Evaluationmentioning

confidence: 99%

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Available transfer capability calculation methods: A comprehensive review

Mohammed

Mustafa

Mohammed

et al. 2019

Int Trans Electr Energ Syst

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Summary The ceaseless efforts by power system industries to promote sustainable and competitive electric power market structure in the deregulated environment have given rise to enormous research in the area of transfer capability of transmission networks. Due to high demand for electricity, transmission components are stressed to operate close to their operating limits, and this leads to a decrease in transmission efficiency. To address this issue, efficient evaluation of available transfer capability (ATC) is crucial for system planning, operation, and control. Several approaches have been proposed for ATC computation. Surprisingly, a comprehensive literature review on ATC computation is yet to be efficiently presented. Researchers have been able to come up with fast algorithms, but most of these algorithms are not accurate, and the presented accurate techniques are not fast enough for online applications. This paper presents a comprehensive review of the different approaches for ATC determination. It provides the concepts, methods, and the features of the ATC. For each technique, the state of the art of the several contributions made by researchers has been highlighted. This review reveals that there are issues regarding ATC calculation methods that need attention: the development of fast and accurate algorithm incorporating system dynamics and system uncertainties in ATC determination. Additionally, efforts on the incorporation of renewable energy generation in the ATC evaluation need to be intensified. This review will serve as one in all for researchers as well as a guide for the entrants in this field.

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Section: Uncertainties In Atc Computationsmentioning

confidence: 99%

Section: Future Directions and Suggestions On Atc Evaluationmentioning

confidence: 99%

Available transfer capability calculation methods: A comprehensive review

Mohammed

Mustafa

Mohammed

et al. 2019

Int Trans Electr Energ Syst

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“…Latin hypercube is stratified sampling scheme that enables better coverage and exploration of the domain of the variations of the input variables. It is preferred over conventional Monte Carlo sampling because of its time-efficiency and faster convergence resulting from its capability to construct efficient probability distributions using less number of iterations and less sampling error (García-Alfonso and Córdova-Esparza, 2018;Gupta and Kumar, 2016). Consequently, fuzzy C-means clustering is utilized to generate the thresholds of the severity levels of spalling area and depth.…”

mentioning

confidence: 99%

Entropy-Based Automated Method for Detection and Assessment of Spalling Severities in Reinforced Concrete Bridges

Abdelkader

Moselhi

Marzouk

et al. 2021

J. Perform. Constr. Facil.

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Existing bridges are aging and deteriorating rapidly; elevating concerns for public safety and preservation of these valuable assets. Large numbers of bridges exist in transportation networks and the current budget limitations worsen the situation. This necessitates the development of automated condition assessment and rating methods. Spalling is a common problem that majorly influences the health, safety and structural integrity of bridges. The present study introduces a self-adaptive three-tier method for the automated detection and assessment of spalling using computer vision technologies. The first model introduces a newly-developed segmentation model that adopts multi-objective invasive weed optimization and information theory-based formalism of images for spalled concrete detection. In the second model, an integration of singular value decomposition and discrete wavelet transform are integrated for the efficient feature extraction of information in images. Additionally, Elman neural network is coupled with invasive weed optimization algorithm to enhance the accuracy of evaluation of spalling severities by amplifying exploration-exploitation trade-off mechanism of Elman neural network. The third model is developed for the purpose of structuring a rating system of spalling severity based on its area and depth. A computerized platform is developed using C#.net language to facilitate the implementation of the developed method by the users. Results demonstrated that the developed multi-objective spalling segmentation model is capable of improving detection accuracy of spalling by 12.29% with respect to the Region growing algorithm. It was inferred also the developed quantification model outperformed other prediction models such that it achieved mean absolute percentage error, root mean-squared error and root mean squared percentage error of 4.07%, 76.061 and 0.065, respectively based on the original dataset. In this regard, it is expected that the developed computer vision-based method can aid in establishing cost-effective bridge condition assessment models by transportation agencies.

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