With the rapid development of flexible DC distribution technology, the flexible interconnected distribution network composed of DC distribution network and AC distribution network has attracted more and more attention...
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ISBN:
(纸本)9781665479141
With the rapid development of flexible DC distribution technology, the flexible interconnected distribution network composed of DC distribution network and AC distribution network has attracted more and more attention of researchers. With the large-scale access of distributed generation with intermittent and random characteristics, the flexible interconnected system presents a certain degree of uncertainty. The traditional power flow analysis method is no longer applicable, so probabilistic load flow analysis method is needed. However, there are few studies on the probabilistic load flow of flexible interconnected distribution systems. Thus, the flow problem of flexible interconnected distribution network is studied with uncertainty in this paper. The three-point estimation method combined with the improved AC/DC alternating iteration algorithm is used as the algorithm for the realization of probabilistic load flow of flexible interconnected distribution network. The Nataf transform method combined with Cholesky decomposition is used to process the correlation of uncertain non-normal input variables. On this basis, the IEEE33 node distribution system combined with the DC power distribution center is taken as an example to verify the effectiveness of the proposed algorithm.
The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using tradit...
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The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs. Specifically, CellMsg first identifies high-confident LRIs based on multimodal features of ligands and receptors and graph convolutional networks. Then, CellMsg measures the strength of intercellular communication by combining the identified LRIs and single-cell RNA-seq data using a three-point estimation method. Performance evaluation on four benchmark LRI datasets by five-fold cross validation demonstrated that CellMsg accurately captured the relationships between ligands and receptors, resulting in the identification of high-confident LRIs. Compared with other methods of identifying LRIs, CellMsg has better prediction performance and robustness. Furthermore, the LRIs identified by CellMsg were successfully validated through molecular docking. Finally, we examined the overlap of LRIs between CellMsg and five other classical CCC databases, as well as the intercellular crosstalk among seven cell types within a human melanoma tissue. In summary, CellMsg establishes a complete, reliable, and well-organized LRI database and an effective CCC strength evaluation method for each single-cell RNA-seq data. It provides a computational tool allowing researchers to decipher intercellular communications. CellMsg is freely available at https://***/pengsl-lab/CellMsg.
With increasing penetration of distributed generations, the importance of the voltage stability assessment of distribution networks has been increased. Considering this situation, a probabilistic model is proposed to ...
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With increasing penetration of distributed generations, the importance of the voltage stability assessment of distribution networks has been increased. Considering this situation, a probabilistic model is proposed to evaluate the voltage stability of distribution network integrating wind turbine (WT) units. With this intention, a probabilistic voltage stability index (PVSI) of a radial distribution system with wind power generation is presented. This index investigates the probabilistic risk of voltage collapse for all the buses of system considering uncertainty of WTs. Therefore, the most sensitive bus to the voltage collapse can be identified. The problem model is based on the catastrophe theory that can find the bifurcation point of system. three-point estimation method is employed to calculate the statistical moments of voltage and PVSI of nodes. Moreover, to estimate the cumulative distribution function of output random variables, the Cornish-Fisher series are used. The performance of the probabilistic index is tested on the IEEE 69-bus radial distribution system where different load models are considered. The results demonstrate that the PVSI can accurately predict the voltage instability condition of the system.
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