Optimal Coordination with Considering Multiple Characteristic Curves

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

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Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques

Applied Sciences ◽

10.3390/app11031241 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1241

Author(s):

Sergio D. Saldarriaga-Zuluaga ◽

Jesús M. López-Lezama ◽

Nicolás Muñoz-Galeano

Keyword(s):

Unsupervised Learning ◽

Distributed Generation ◽

Network Topology ◽

International Electrotechnical Commission ◽

Number Of Clusters ◽

Learning Techniques ◽

Topology Changes ◽

Network Topologies ◽

Optimal Coordination ◽

Operational Modes

Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach.

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Derivation of water flooding characteristic curve for offshore low-amplitude structural reservoir with strong bottom water

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01240-z ◽

2021 ◽

Author(s):

Ying-xian Liu ◽

Jie Tan ◽

Hui Cai ◽

Yan-lai Li ◽

Chun-yan Liu

Keyword(s):

Bottom Water ◽

Characteristic Curve ◽

Calculated Data ◽

Oil Production ◽

Water Flooding ◽

Characteristic Curves ◽

Recoverable Reserves ◽

Curve Method ◽

Water Cut ◽

Low Amplitude

AbstractThe water flooding characteristic curve method is one of the essential techniques to predict recoverable reserves. However, the recoverable reserves indicated by the existing water flooding characteristic curves of low-amplitude reservoirs with strong bottom water increase gradually, and the current local recovery degree of some areas has exceeded the predicted recovery rate. The applicability of the existing water flooding characteristic curves in low-amplitude reservoirs with strong bottom water is lacking, which affects the accurate prediction of development performance. By analyzing the derivation process of the conventional water flooding characteristic curve method, this manuscript finds out the reasons for the poor applicability of the existing water flooding characteristic curve in low-amplitude reservoir with strong bottom water and corrects the existing water flooding characteristic curve according to the actual situation of the oilfield and obtains the improvement method of water flooding characteristic curve in low-amplitude reservoir with strong bottom water. After correction, the correlation coefficient between $$\frac{{k_{ro} }}{{k_{rw} }}$$ k ro k rw and $$S_{w}$$ S w is 95.92%. According to the comparison between the actual data and the calculated data, in 2021/3, the actual water cut is 97.29%, the water cut predicted by the formula is 97.27%, the actual cumulative oil production is 31.19 × 104t, and the predicted cumulative oil production is 31.31 × 104t. The predicted value is consistent with the actual value. It provides a more reliable method for predicting low-amplitude reservoirs' recoverable ability with strong bottom water and guides the oilfield's subsequent decision-making.

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