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Electric spring (ES) is an emerging power quality adjustment method that can effectively solve the power quality problem, especially voltage fluctuations caused by renewable energy sources. However, the existing topologies of ES have the shortcoming of limited compensation range due to their structure, in which ES is in series with non-critical load (NCL). In this paper, a novel ES is proposed based on a passively damped LCL filter. Unlike the existing ES topologies, LCL-ES employs NCL to implement passive damping of LCL filter, which not only overcomes the passive damping but also extends the compensation range. Besides, the key points on topology design and control strategy are discussed. Finally, the effectiveness of the proposed LCL-ES has been verified via simulation.

We show here some of our results on intuitionistic fuzzy topological spaces. In 1983, K.T. Atanassov proposed a generalization of the notion of fuzzy set: the concept of intuitionistic fuzzy set. D. Çoker constructed the fundamental theory on intuitionistic fuzzy topological spaces, and D. Çoker and other mathematicians studied compactness, connectedness, continuity, separation, convergence and paracompactness in intuitionistic fuzzy topological spaces. Finally, G.-J Wang and Y.Y. He showed that every intuitionistic fuzzy set may be regarded as an L-fuzzy set for some appropriate lattice L. Nevertheless, the results obtained by above authors are not redundant with other for ordinary fuzzy sense. Recently, Smarandache defined and studied neutrosophic sets (NSs) which generalize IFSs. This author defined also the notion of neutrosophic topology. We proved that neutrosophic topology does not generalize the concept of intuitionistic fuzzy topology.

The reduction theory is the most significant component of rough set theory. This paper for the first time employs the topological separability to analyze the reductions of covering rough sets. First, a definition is given to the covering separability to describe the classification ability of knowledge bases. Second, a connection is built between the separability and discernibility matrix. The knowledge bases which do not satisfy the separability are transformed to ones with separability via the application of the topological method, and then discernibility matrices with lower orders are reached. As a significant advantage, the method simplifies discernibility matrices to lower order, and in turn improves all reduction algorithm based on discernibility matrix.