Nowadays Nb3Sn wires are being widely used as one of the key materials in advanced science and technology, with various applications such as NMR, fusion and cryogen-free superconducting magnets. In this article, at first microstructures and fundamental aspects as well as the effect of additional elements in Nb3Sn are outlined. Intrinsic superconducting performances, e.g. Tc and Bc2, are quite sensitive to the stoichiometry of the Sn concentration. A small amount of Ti and Ta doping is much effective for the increase of Bc2 in Nb3Sn. The effect of Cu on the enhancement of Nb3Sn synthesis has yielded a significant breakthrough for the industrial production of the wires. At present the bronze process and internal Sn process are the twin major fabrication techniques of Nb3Sn wires. The recent status of both processes is reviewed in this article. Pronounced progress has been achieved in the performance of Nb3Sn wires fabricated through both techniques. Although just half a century has passed since the first fabrication of Nb3Sn wire, further progress in Nb3Sn technology may be expected like the proverb saying “Fresh water still comes out from an old spring”.
In photovoltaic (PV) and wind turbine (WT) power generation, which are typical examples of clean power, the amount of power generated changes rapidly and constantly according to the stochastic weather conditions. In order to ensure a high-quality power supply, including constant voltage and current, it is necessary to compensate for the power fluctuations. A system combining fuel cells (FCs), which can generate power for long periods of time, and a superconducting magnetic energy storage (SMES) system, which can produce large amounts of power quickly, can potentially function to compensate the power fluctuations all the time. In this paper, since WT power is resolved into trend and fluctuating components, the former is provided by FCs, and the latter is provided by the SMES system. The trend components are predicted using a Kalman filter, which describes the state-space model for WT power generation. Since the SMES system cost is very high, it is important to optimize the compensation capacity of SMES in the combined system. We found that the trend prediction method, a modification of the Kalman filter, allows us to optimize the SMES capacity.