A New Model of Numerical MHO Distance Relay Associated with Power Swing Detector

In conventional transmission line protection, a distance relay is used to provide the primary as well as backup protection. The voltage and current phasors measurement needed by the distance relay for determining the impedance may be affected by the power disturbances such as power swing. Consequently, this power swing may cause mal-operation of Zone three distance relays which in turn may affect on the reliability of the whole protective scheme. Many power swing blocking functions (PSB) have been developed to mitigate these effects. In this paper, a new model of Mho distance relay with dual-quadrilateral power swing detection characteristic has been developed and implemented first in PC using LabVIEW, then tested using Power System Simulink Model under different faults and power swing conditions. Finally, the relay prototype has been realized using acquisition card NI USB-6009, which acquires real-time signals of the currents and the voltages, processes them digitally and outputs tripping or blocking signal to the circuit breaker. The obtained results show that the relay provides good discrimination between a fault and power swing condition.

Ground-Based Corroboration of GOES-17 Fire Detection Capabilities During Ignition of the Kincade Fire

Corroboration of Geostationary Operational Environmental Satellite-17 (GOES-17) wildland fire detection capabilities occurred during the 24 October 2019 (evening of 23 October LST) ignition of the Kincade Fire in northern California. Post-analysis of remote sensing data compared to observations by the ALERTWildfire fire surveillance video system suggests that the emerging Kincade Fire hotspot was visually evident in GOES17 shortwave infrared imagery 52 s after the initial near-infrared heat source detected by the ground-based camera network. GOES-17 Advanced Baseline Imager Fire Detection Characteristic algorithms registered the fire 5 min after ignition. These observations represent the first documented comparative dataset between fire initiation and satellite detection, and thus provide context for GOES-16/17 wildland fire detections.

Analysis of the Optimal Wavelength for Oceanographic Lidar at the Global Scale Based on the Inherent Optical Properties of Water

Understanding the optimal wavelength for detecting the water column profile from a light detection and ranging (lidar) system is important in the design of oceanographic lidar systems. In this research, the optimal wavelength for detecting the water column profile using a lidar system at the global scale was analyzed based on the inherent optical properties of water. In addition, assuming that the lidar system had a premium detection characteristic in its hardware design, the maximum detectable depth using the established optimal wavelength was analyzed and compared with the mixed layer depth measured by Argo data at the global scale. The conclusions drawn are as follows: first, the optimal wavelengths for the lidar system are between the blue and green bands. For the open ocean, the optimal wavelengths are between 420 and 510 nm, and for coastal waters, the optimal wavelengths are between 520 and 580 nm. To obtain the best detection ability using a lidar system, the best configuration is to use a lidar system with multiple bands. In addition, a 490 nm wavelength is recommended when an oceanographic lidar system is used at the global scale with a single wavelength. Second, for the recommended 490 nm band, a lidar system with the 4 attenuating length detection ability can penetrate the mixed layer for 80% of global waters.

A transmittance-based methodology for damage detection under uncertainty: An application to a set of composite beams with manufacturing variability subject to impact damage and varying operating conditions

Oftentimes, the complexity in manufacturing composite materials leads to corresponding structures which although they may have the same design specifications they are not identical. Thus, composite parts manufactured in the same production line present differences in their dynamics which combined with additional uncertainties due to different operating conditions may lead to the complete concealment of effects caused by small, incipient, damages making their detection highly challenging. This damage detection problem in nominally identical composite structures is pursued in this study through a novel data-based response-only methodology that is founded on the autoregressive with exogenous (ARX) excitation parametric representation of the transmittance function between vibration measurements at two different locations on the structure. This is a statistical time series methodology within which two schemes are formulated. In the first, a single-reference transmittance model representing the healthy structure is employed, while multiple transmittance models from a sample of available healthy structures are used in the second. The model residual signal constitutes for both schemes the damage detection characteristic quantity that is used in appropriate hypothesis testing procedures with the likelihood ratio test. The methodology is experimentally assessed via damage detection for a population of composite beams which are manufactured in the same production line representing the half of the tail of a twin-boom unmanned aerial vehicle. The damage detection results demonstrate the superiority of the multiple transmittance models based scheme that may effectively detect damages under significant manufacturing variability and varying boundary conditions.