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Intelligent distributed protection and control is a promising route towards flexible and safe operation of the medium-voltage distribution network with widespread access to distributed energy resources. A fundamental premise of distributed decision-making is that each smart terminal unit (STU) can identify the topology of the feeder and track its changes. A distributed topology identification algorithm with high fault tolerance based on peer-to-peer communication is proposed in this paper. The STU configured at the looped network node requires no pre-set static topology of the feeder group, but only relies on local measurement and information exchange with neighboring STUs. Through rule judgment, it can realize the dynamic tracking and monitoring of the complete topology and open-loop mode of the feeder group, which reduces the configuration requirements of the algorithm on the terminal. Meanwhile, the method only needs each STU to exchange the local measurement and judgment information, which reduces the requirement of communication. Test examples show that the proposed method is reasonable and effective, and it can significantly improve the tolerance of topological identification to measurement errors by combining local measurement mutual checking and contralateral connectivity predictive correction.

With the existing reliability evaluation methods, it is difficult to realize the overall evaluation of multi-voltage levels distribution systems, and sense-control terminal faults are not considered in the process of reliability evaluation. Therefore, a reliability evaluation method for multi-voltage levels distribution networks considering the influence of the fault of the sensing and control terminal is proposed. Firstly, based on the component reliability model, system state selection method, and reliability index, a reliability evaluation method for distribution networks considering sense-control terminal faults has been proposed. Secondly, the features of a multi-voltage levels distribution network are analyzed. The sequential Monte Carlo simulation method and the failure mode effects analysis method are combined, and a collaborative evaluation method for power supply reliability of multi-voltage levels distribution systems is proposed. Eventually, the effectiveness of the proposed method is proved by an example comparison, and the impact of the terminal fault on the reliability of distribution network is analyzed. The reliability evaluation results can provide technical support for the planning of the distribution network. [ABSTRACT FROM AUTHOR]

Objectives This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and to find potential underlying pathological mechanisms therefrom. Methods Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as centre 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as centre 2 (external validation set), including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by ultra high performance liquid chromatography plus Q-Exactive mass spectrometry (UHPLC-Q Exactive MS). Differential metabolites were identifed by both variable importance in the projection ≥1 in orthogonal partial least-squares discriminant analysis mode and false discovery rate adjusted P ≤ 0.05. Biomarkers were found by logistic regression and receiver operating characteristic (ROC) analysis. Results In the discovery set, a total of 245 and 150 metabolites, respectively, were found for normal subjects vs HUA and normal subjects vs gout. The disturbed metabolites included diacylglycerol, triacylglycerol (TAG), phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, etc. We also found 116 differential metabolites for HUA vs gout. Among them, the biomarker panel of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 could differentiate well between HUA and gout. The area under the receiver operating characteristic ROC curve was 0.8288, the sensitivity was 82% and the specificity was 78%, at a 95% CI 0.747, 0.9106. In the internal validation set, the predictive accuracy of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 panel for differentiation of HUA and gout reached 74.38%, while it was 84.03% in external validation set. Conclusion We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients. [ABSTRACT FROM AUTHOR]

The rapid growth of distributed energy resources integrated in distribution systems leads to an increasing need of continuously and automatically changing the system topology to realize the economic operation of distribution networks. This paper proposes an optimization model of dynamic reconfiguration for distribution networks based on a new method of time series analysis. Equivalent daily curve considering time‐varying nature of distributed generator and load demands is divided by an improved fuzzy C‐means clustering algorithm, where the indicator of section function is set to find the optimal reconfiguration time intervals. The uncertainty of distributed generator outputs and load demands is described by the interval algorithm. Then the affine Taylor expansion is adopted to solve the interval power flow equations. The reconfiguration optimization model is solved with decimal particle swarm optimization algorithm based on loop search. The optimal dynamic reconfiguration of a modified 70‐bus test system with distributed generators is carried out and the simulation results demonstrate the effectiveness and superiority of the proposed method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]