భౌతిక గణితం

Multi-courier astronomy (MMA) has emerged as a result of the IceCube Neutrino Observatory's discovery of an extra-cosmic neutrino source and the early identification of gravitational wave (GW) sources. In addition to the simultaneous perception of incidental gamma-beam photons followed by photons at other electromagnetic (EM) frequencies, these findings provide fresh insights into the physical science of the universe. Strong MMA proposals are anticipated, even though the 2020 Astronomy Decadal Review report has not yet been released. Modern ground-based observatories will significantly increase the number of sources that require brief EM follow-up in space and broaden the landscape of discovery in the coming years. The requirements for the MMA group will significantly overlap.

Distribution network state estimation is based on distribution network (DN) topology identification (TI). The connection of high-penetration renewable energy, on the other hand, makes TI of DN more difficult. This manuscript therefore proposes an active distribution network (ADN) TI method based on a one-dimensional convolutional neural network. The characteristics of the nodes are analyzed in light of the sensitivity of node voltage to DN topology changes in order to select the key nodes where the distribution network phasor measurement unit (DPMU) should be placed. This can save money on investment and make model training less redundant. Using photovoltaic (PV) units and a modified IEEE-33 bus DN, a number of tests are carried out. Under limited DPMU measurement, the results demonstrate that the proposed distribution network topology identification method can achieve high accuracy TI in ADN.