Detection Efficiency Analysis of Atmospheric Discharges Using AS3935 Sensor : Data Correlation of LINET Network

Heilmann, Armando; Silva, Elielton Matias da; Filho, Horácio Tertuliano; Schuhmann, Joice Cristine; Adams, Augusto Mathias; Dartora, C. A.; Lima, David Clístenes Furoni de; Burkarter, E.

doi:10.1109/sipda47030.2019.8951657

Cited by 1 publication

(5 citation statements)

References 14 publications

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“…Lightning data is generated by a simulation script that, considering a selected random areas called storms (are circles with a given radius), inserts lightning in storms and inserts detection events obtained from lightning into Storm Detector Network , using an hypothetical sensor network deployed on the area of Paraná state. The simulation takes in consideration the parameters found in [1] for sensor distance estimation, given as:…”

Section: Lightning Datamentioning

confidence: 99%

“…Where, LE represents the natural logarithm of the sensor energy parameter and δ is the distance in km. The simulation also takes advantage of the Absolute Detection Efficiency (ADE) curve also found by [1] to compute the detection probability of each hypothetical sensor, given as: ADESD(δ) = 88.11 (1 -1/(1 + 309.6 EXP(-0.2888 δ)) .…”

Section: Lightning Datamentioning

confidence: 99%

“…For each lightning event, the simulation computes the distance to each sensor, then computes the detection probability also for each sensor and determine the sensors in detection state doing a simple Bernoulli trial, using the output of a random generator to compare with the probabilities given by (2). The sensor location error [1] of +/-3km is inserted to each sensor computed distance as an additive gaussian noise. The simulation sends the data of each sensor to Storm Detector Network server using MQTT protocol, using a JavaScript-Object Notation (JSON) package defined by Storm Detector Network as a detection package.…”

Section: Lightning Datamentioning

confidence: 99%

“…Last year the Group of Atmospheric Electricity Phenomena (FEA/UFPR) developed a short range network prototype using a sensor device called Storm Detector device (SD) [1], which was formerly used mainly for research purposes, equipped with an AS3935 sensor [4] for lightning detection, controlled by an ESP32 [5], a System-On-Chip (SoC) microprocessor. It also embeds a GPS device for location/time synchronization and a small storage device for data logging.…”

Section: Introductionmentioning

confidence: 99%

“…The LLN performance parameters are defined to at least Detection Efficiency (DE), Location Accuracy (LA) and Classification Accuracy (CA) [7]. The Storm Detector Device was designed to be a low-cost alternative to access storm risks and alerts in large areas where communication and/or financial resources are a major problem, such as rural areas, remote electricity facilities, oil refineries, airports, tourist areas, electrical systems and populations close to mountainous regions, offering acceptable performance for such tasks, such as reasonable LA and real-time operation [1].…”

Section: Introductionmentioning

confidence: 99%

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TrackingStorm: Visualization Tool for a Storm Detector Network (SDN) in the LF Spectrum

Adams

Heilmann

Adams³

et al. 2021

Braz. arch. biol. technol.

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During the last year the Group of Atmospheric Electricity Phenomena (FEA/UFPR) developed a short range lightning location network based on a sensor device called Storm Detector Network (SDN), along with a set of algorithms that enables to track storms, determining the Wide Area Probability (WAP) of lightning occurrence, risk level of lightning and Density Extension of the Flashes (DEF), using the geo-located lightning information as input data. These algorithms compose a Dashboard called Tracking Storm Interface (TSI), which is the visualization tool for an experimental short range Storm Detector network prototype in use on the region of Curitiba-Paraná, Brazil. The algorithms make use of Geopandas and clustering algorithms to locate storms, estimate centroids, determine dynamic storm displacement and compute parameters of thunderstorms like velocity, head edge of electrified cloud, Mean Stroke Rate, and tracking information, which are important parameters to improve the alert system which is subject of this research. To validate these algorithms we made use of a simple storm simulation, which enabled to test the system with huge amounts of data. We found that, for long duration storms, the tracking results, velocity and directions of the storms are coherent with the values of simulation and can be used to improve an alert system for the Storm Detector network. WAP can reach at least 75% of prediction efficiency when used 6 past WAP data, but can reach

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Section: Lightning Datamentioning

confidence: 99%

Section: Lightning Datamentioning

confidence: 99%

Section: Lightning Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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