Photovoltaic (PV) string exhibits complex multiple-peak characteristics under various partial shading conditions (PSC). If the maximum power point tracking cannot be achieved quickly and accurately, it will lead to a large amount of energy loss. Therefore, it has become a hot topic to study a reliable maximum power tracking control algorithm to ensure the PV system can still output maximum power under PSC. This paper proposes an immune firefly algorithm (IFA), which utilizes vaccine data-base to shorten the convergence time, eliminates the influence of bad individuals in time by immune replenishment operation, and reduces the steady-state oscillation by the improving iteration formula. The simulations in static and dynamic environments verify that the immune firefly algorithm can track the maximum power point under various partial shading conditions. Compared with conventional firefly algorithm (FA), IFA has faster convergence speed, and can effectively restrain the oscillation of voltage and power.
About 400 mDOMs (multi-PMT Digital Optical Modules) will be deployed as part of the IceCube Upgrade project. The mDOM's high pressure-resistant glass sphere houses 24 photomultiplier tubes (PMTs), 3 cameras, 10 flasher LEDs and various sensors. The mDOM mainboard design was challenging due to the limited available volume and demanding engineering requirements, like the maximum overall power consumption, a minimum trigger threshold of 0.2 photoelectrons (PE), the dynamic range and the linearity requirements. Another challenge was the FPGA firmware design, handling of about 35 Gbit/s of continuous ADC data from the digitization of the 24 PMT channels, the control of a high speed dynamic buffer and the discriminator output sampling rate of about 1 GSPS. High-speed sampling of each of the discriminator outputs at ∼1 GSPS improves the leading-edge time resolution for the PMT waveforms. An MCU (microcontroller unit) coordinates the data taking, the data exchange with the surface and the sensor readout. Both the FPGA firmware and MCU software can be updated remotely. After discussing the main hardware blocks and the analog frontend (AFE) design, test results will be shown, covering especially the AFE performance. Additionally, the functionality of various sensors and modules will be evaluated.
The IceCube Neutrino Observatory is also a very unique extensive air shower (EAS) detector, that simultaneously measures the EAS footprint on the surface and the high-energy muons in deep ice. The surface array -IceTop, comprising of ice-Cherenkov tanks, will be enhanced in the coming years with scintillation detectors and radio antennas. The hybrid detection enables the reconstruction of EAS parameters based on different underlying signal distributions. A new framework within the IceCube software allows for a flexible implementation of signal and time models for different detector components and a combination of resulting likelihood functions. The in-ice muon signal can serve as an anchor for the reconstruction of the EAS axis, resulting in an improved reconstruction resolution. Moreover, it makes it possible to reconstruct EASs with an impact point outside the IceTop array, opening a larger zenith-angle range for analyses of IceTop and in-ice coincident events. In this contribution, we present the capabilities of the combined reconstruction for different classes of EAS events with various detector configurations.
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