Uncertainties of renewable energy sources like wind power are one of the considerable challenges of prosumer microgrids. To meet the grid codes requirements regarding the voltage stability of wind farm integration, finding the balance between providing the demanding dynamic performance of the voltage and reactive power, and at the same time decreasing the investment on centralized reactive power compensation device, becomes an important research topic. This paper compares the effects of the static synchronous compensator (STATCOM) and static VAR compensator (SVC) on transient voltage stability at the point of common coupling (PCC) of a doubly fed induction generator (DFIG)-based wind farm. And a new fast coordinated control scheme of STATCOM and DFIG is proposed for minimizing the capacity of centralized reactive power compensation device and making the best use of the reactive power control capability of DFIG. The simulation results based on test system show that STATCOM, even with less capacity, can contribute more reactive power than SVC for voltage stability, especially during the serious voltage drop transient stage, and perform a faster voltage recovery time after fault than SVC, proved to be a more economic choice; The proposed coordinated control scheme can not only improve the transient voltage stability, but also help reducing the capacity of STATCOM, so that the cost of investments in wind farms would be reduced.
Disperse engineering information constitutes a domain of complex relationship, into which researching with complex network could contribute significantly to its organization and preservation. A complex network which takes engineering software data formats as nodes is constructed and its statistical characteristics, such as directed edge or undirected edge, the node number, the edge number, average path length, clustering coefficient and node degree distribution, etc, are analyzed in this paper. A Voronoi diagram based complex network visualization and retrieval method is provided. According to statistical characteristics of constructed network, visualization method calculates network nodes’ position in a two-dimensional plane and divides this plane into Voronoi diagram by feature nodes of network nodes’ clustering and network nodes. Retrieval method restricts compared nodes to nodes in Voronoi domain of feature nodes which are more related to query node, the number of compared nodes is reduced in retrieval process. The experiment result indicates applying this method into data formats conversion path retrieval can ensure retrieval precision and improve retrieval efficiency, thus provide reliable basis for migrating numerous disperse engineering information.
With the development of science and technology, the interactions among scientific research teams become more and more frequent, and their relationship and behavior become more and more complex. Many researches mainly adopt complex network to analyze, but these researches only consider some aspects of scientific research factors, so lack of comprehensive consideration. From the aspect of ability, resource, activity, and familiarity, scientific research factors are quantified based on multi-source data of scientific and technological big data, and some factors of text information are similarly quantified. Based on paper citation and project cooperation, a complex network which takes scientific research team as node is constructed and is weighted by quantification of scientific research factor. The experiment of influence spread is carried out by the comparison of unweighted network and weighted network, the comparison of single node and multiple nodes, and the comparison of influence spread and other index. The results show that the scientific research factor is closely related to the influence spread; the proposed scientific research factor quantification improves the analysis of scientific research team relationship. The relationship between influence spread and the number of related communities is greater than the number of adjacent nodes. In addition, the influence spread can effectively reflect the importance of scientific research team.
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