The rapid development of Internet Plus Smart Energy requires further strengthening of three kinds of interconnections based on traditional power systems: physical interconnection, information ...interconnection, and commercial interconnection. Due to the integration of renewable energy, the reform of the electricity market, and the deployment of the Smart Grid, a large amount of data will be generated. Thus, it is necessary to establish a Ubiquitous Power Internet of Things (UPIoT) to realize connections among people and things, things and things, and people and people in power systems. This paper studies the concept and architecture of the UPIoT and indicates the deployment of the perception layer and network layer as the key to building UPIoT in the initial stage. As UPIoT tends to cover a wide area and produce massive and distributed data, signal processing and data analytics theories and techniques are needed to handle the data and observe the state of the large-scale system. Further studies on distributed sensing and cooperative estimation theories and techniques of UPIoT are also required. Finally, the application prospects of UPIoT and the directions for future research are discussed.
Abstract Type Icn supernovae (SNe Icn) are a newly detected, rare subtype of interacting stripped-envelope supernovae that show narrow P Cygni lines of highly ionized carbon, oxygen, and neon in ...their early spectra due to the interactions of the SNe ejecta with dense hydrogen- and helium-deficient circumstellar material (CSM). It has been suggested that SNe Icn may have multiple progenitor channels, such as the explosion of carbon-rich Wolf–Rayet stars or the explosion of stripped-envelope SNe, which undergo binary interactions. Among the SNe Icn, SN 2019jc shows unique properties, and previous work inferred that it may stem from the ultrastripped supernova, but other possibilities still exist. In this work, we aim to simulate the light curves from the explosions of oxygen-neon and carbon-oxygen double white dwarf (WD) merger remnants and to further investigate whether the corresponding explosions can appear as some particular SNe Icn. We generate the light curves from the explosive remnants and analyze the influence of different parameters on the light curves, such as the ejecta mass, explosion energy, mass of 56 Ni, and CSM properties. Comparing our results with some SNe Icn, we found that the light curves from the explosions of double WD merger remnants can explain the observable properties of SN 2019jc, from which we infer that this special SN Icn may have a different progenitor. Our results indicate that double WD merger may be an alternative model in producing at least one of the SNe Icn.
In the last 20 years, modern wide-field surveys discovered a new class of peculiar transients, which lie in the luminosity gap between standard supernovae and classical novae. These transients are ...often called “intermediate luminosity optical transients” or “gap transients”. They are usually distinguished in subgroups based on their phenomenology, such as supernova impostors, intermediate luminosity red transients, and luminous red novae. In this review, we present a brief overview of their observational features and possible physical scenarios to date, in the attempt to understand their nature.
Overvoltages are the main causes of damages and accidents in electric power grids. The traditional approach is to install some static protection devices that are passive and cannot identify the ...overvoltage types or locate where the overvoltage event occurs. In recent years, with the development of smart grids, some online overvoltage monitoring systems have been developed. However, the approaches to data processing still need further development. A novel technique for identification and location of overvoltages in power distribution systems is proposed, which uses capacitor bank energization overvoltages (CBOVs) and ground fault temporary overvoltages (TOVs) as the study cases. The wavelet packet decomposition (WPD) theory is used for frequency band decomposition, and a general regression neural network (GRNN) is used in identification and location. Simulation results based on real-world power distribution systems show that the method is accurate and fast.
Abstract SN 2023ixf, recently reported in the nearby galaxy M101 at a distance of 6.85 Mpc, was one of the closest and brightest core-collapse supernovae in the last decade. In this work, we present ...multiwavelength photometric observation of SN 2023ixf with the Multi-channel Photometric Survey Telescope (Mephisto) in the uvgr bands and with the twin 50 cm telescopes in the griz bands. We find that the bolometric luminosity reached the maximum value of 3 × 10 43 erg s −1 at 3.9 days after the explosion and fully settled onto the radioactive tail at ∼90 days. The effective temperature decreased from 3.2 × 10 4 K at the first observation and approached a constant of ∼(3000–4000) K after the first 2 months. The evolution of the photospheric radius is consistent with a homologous expansion with a velocity of 8700 km s −1 in the first 2 months, and it shrunk subsequently. Based on the radioactive tail, the initial nickel mass is about M Ni ∼ 0.098 M ⊙ . The explosion energy and the ejecta mass are estimated to be E ≃ (1.0–5.7) × 10 51 erg and M ej ≃ (3.8–16) M ⊙ , respectively. The peak bolometric luminosity is proposed to be contributed by the interaction between the ejecta and the circumstellar medium (CSM). We find a shocked CSM mass of M CSM ∼ 0.013 M ⊙ , a CSM density of ρ CSM ∼ 2.5 × 10 −13 g cm −3 , and a mass-loss rate of the progenitor of M ̇ ∼ 0.022 M ⊙ yr − 1 .
Abstract We present early-time, hour-to-day cadence spectroscopy of the nearby Type II supernova (SN II) 2024ggi, which was discovered at a phase when the SN shock had just emerged from the red ...supergiant (RSG) progenitor star. Over the first few days after the first light, SN 2024ggi exhibited prominent narrow emission lines formed through intense and persistent photoionization of the nearby circumstellar material (CSM). In the first 63 hr, spectral lines of He, C, N, and O revealed a rapid rise in ionization as a result of the progressive sweeping up of the CSM by the shock. The duration of the IIn-like spectra indicates a dense and relatively confined CSM distribution extending up to ∼4 × 10 14 cm. Spectral modeling reveals that a CSM mass-loss rate at this region exceeding 5 × 10 −3 M ⊙ yr −1 is required to reproduce low-ionization emissions, which dramatically exceeds that of an RSG. Analyzing the H α emission shift implies the velocity of the unshocked outer CSM to be between 20 and 40 km s −1 , matching the typical wind velocity of an RSG. The differences between the inner and outer layers of the CSM and an RSG progenitor highlight a complex mass-loss history before the explosion of SN 2024ggi.
Abstract
ASASSN-14ms may represent the most luminous Type Ibn supernova (SN Ibn) ever detected, with an absolute
U
-band magnitude brighter than −22.0 mag and a total bolometric luminosity >1.0 × 10
...44
erg s
−1
near maximum light. The early-time spectra of this SN are characterized by a blue continuum on which are superimposed narrow P Cygni profile lines of He
i
, suggesting the presence of slowly moving (∼1000 km s
−1
), He-rich circumstellar material (CSM). At 1–2 months after maximum brightness, the He
i
line profiles become only slightly broader, with blueshifted velocities of 2000–3000 km s
−1
, consistent with the CSM shell being continuously accelerated by the SN light and ejecta. Like most SNe Ibn, the light curves of ASASSN-14ms show rapid post-peak evolution, dropping by ∼7 mag in the
V
band over three months. Such a rapid post-peak decline and high luminosity can be explained by interaction between SN ejecta and helium-rich CSM of 0.9
M
⊙
at a distance of ∼10
15
cm. The CSM around ASASSN-14ms is estimated to originate from a pre-explosion event with a mass-loss rate of 6.7
M
⊙
yr
−1
(assuming a velocity of ∼1000 km s
−1
), which is consistent with abundant He-rich material violently ejected during the late Wolf–Rayet (WN9-11 or Opfe) stage. After examining the light curves for a sample of SNe Ibn, we find that the more luminous ones tend to have slower post-peak decline rates, reflecting that the observed differences may arise primarily from discrepancies in the CSM distribution around the massive progenitors.
This paper examines how we can combine two big trends in solar energy: the spread of solar panels and wind turbines to renew the power grid, and cloud and edge computing technology to improve the way ...the grid works. Our study introduces a new strategy that is based on a means to exploit the power of cloud computing’s big data handling ability, together with the capacity of edge computing to provide real-time data processing and decision making. The method is designed to address major challenges in renewables systems making the system bigger and more reliable, and cutting the time delays in deciding how the system should respond. These are the kinds of changes that will be necessary so that we can blend solar and wind power into our current power grid, whether we are ready to say goodbye to coal or natural gas power. Our paper presents a way in which we believe that renewables systems can work more smoothly and effectively. This includes making it easier to measure how much power is being generated, to control these systems so that they function much like traditional power plants, and hence, to allow renewable energy to be part of a reliable and efficient part of our electricity supply. These are all crucial steps in using technology to make more of the green power from the sun – which we must do for our energy usage to be more earth friendly.
The distribution network line loss computation method needs to be enhanced in light of the ongoing growth of the national power grid. The study classifies and segments the station data using a ...decision tree model and a multi-feature volume weighted station clustering algorithm. It then uses a back propagation neural network as a substrate, along with the Levenberg-Marquard algorithm and genetic algorithm for optimization. Collect and organize relevant data on line loss rates in low-voltage substation areas, including information on energy meters, meter boxes, and lines. Next, construct a genetic algorithm neural network model and use the backpropagation algorithm for training. Evaluate the accuracy and stability of various models by comparing the error between predicted and actual line loss rates through experiments. Finally, optimize the neural network parameters and network structure to improve the model's prediction accuracy and robustness. The experimental data showed that compared to the density-based spatial clustering algorithm for noisy applications, the contour coefficient metrics of the proposed multi-feature volume weighted station clustering algorithm improved by 0.05 and the average consumption time of the algorithm was reduced by 75%. Compared to the back-propagation neural network model optimized by the Levenberg-Marquard algorithm, the root-mean-square error of the neural network model optimized by the addition of the genetic algorithm for the calculation of the line loss rate of the four station samples was reduced by 72%, 55%, 53% and 37%, and the values of R2 were improved by 8.72%, 13.59%, 7.91% and 11.69%, respectively. The testing results demonstrated that the neural network model has good generalization capabilities and a high degree of curve fitting. Also, the relative errors of the calculation of the station area's line loss rate are mainly within the range of 0% and 10%. For the growth of energy conservation in the country, this innovative technology offers a new way to determine and manage line loss of the station area.
Abstract
We present the discovery and studies of the helium-rich, fast-evolving supernova (SN) 2021agco at a distance of ∼40 Mpc. Its early-time flux is found to rise from half peak to the peak of ...−16.06 ± 0.42 mag in the
r
band within
2.4
−
1.0
+
1.5
days, and the post-peak light curves also decline at a much faster pace relative to normal stripped-envelope supernovae (SNe) of Type Ib/Ic. The early-time spectrum of SN 2021agco (
t
≈ 1.0 days after the peak) is characterized by a featureless blue continuum superimposed with a weak emission line of ionized C
iii
, and the subsequent spectra show prominent He
i
lines. Both the photometric and spectroscopic evolution show close resemblances to SN 2019dge, which is believed to have an extremely stripped progenitor. We reproduce the multicolor light curves of SN 2021agco with a model combining shock-cooling emission with
56
Ni decay. The best-fit results give an ejecta mass of ≈0.3
M
⊙
and a synthesized nickel mass of ≈2.2 × 10
−2
M
⊙
. The progenitor is estimated to have an envelope radius of
R
env
≈ 80
R
⊙
and a mass of
M
env
≈ 0.10
M
⊙
. All these suggest that SN 2021agco can be categorized as an ultra-stripped SN Ib, representing the closest object of this rare subtype. This SN is found to explode in the disk of a Sab-type galaxy with an age of ∼10.0 Gyr and low star-forming activity. Compared to normal SNe Ib/c, the host galaxies of SN 2021agco and other ultra-stripped SNe tend to have relatively lower metallicity, which complicates the properties of their progenitor populations.
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