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Construction and Testing of Lightning Dectector
Subject area: Science,Engineering and Technology · Area of research: Physics with Electronics
Abstract
Lightning strikes are a serious hazard to public safety since they can cause major damage to both public and private infrastructure. There is a need for ongoing research on lighting, which is becoming more dynamic owing to climate change, especially in storm-prone nations, in order to guaranty the safety of facilities and assess the causes of lightning incidents. In Nigeria, lighting detection devices are found in meteorological stations where lighting incidents are not tracked. When they are, bureaucratic procedures frequently make it difficult to obtain data for scientific research. Therefore, a fabricated one that may be utilized for scholarly and experimental research is required. A fabricated lighting detector was built to track and monitor instances of lightning strikes. Lightning detector, WiFi module, battery charger, reel time clock (RTC), CMOS, and lithium batteries are among its various sections. The module's microprocessor processes the lightning detector's digital outputs, which are then recorded on a data logger for processing and data interpretation. The gadget was set up, and throughout the two months of April and May 2025, lightning data was gathered. The number of occurrences in connection to the number of days was examined using a regression model. The data was validated using the regression coefficient R2. The degree of fitness findings for linear, quadratic, and cubic, respectively, are 0.968, 0.971, and 0.971 for April and 0.979, 0.980, and 0.983 for May. The aforementioned results are well-fitting models that can be used to estimate the frequency of lightning strikes. The detector's memory, low power consumption, small size, mobility, and affordability are just a few of its numerous benefits.
Keywords
Lightning Detector, Occurrences, Regression Model, Wi-Fi Module
How to cite this paper
@article{1722386,
author = {Olawoore, T. O., Olawoore, S. A., Akintunde, Z. T., Atilola, O. S.},
title = {Construction and Testing of Lightning Dectector},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {1526-1533},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722386.pdf},
abstract = {Lightning strikes are a serious hazard to public safety since they can cause major damage to both public and private infrastructure. There is a need for ongoing research on lighting, which is becoming more dynamic owing to climate change, especially in storm-prone nations, in order to guaranty the safety of facilities and assess the causes of lightning incidents. In Nigeria, lighting detection devices are found in meteorological stations where lighting incidents are not tracked. When they are, bureaucratic procedures frequently make it difficult to obtain data for scientific research. Therefore, a fabricated one that may be utilized for scholarly and experimental research is required. A fabricated lighting detector was built to track and monitor instances of lightning strikes. Lightning detector, WiFi module, battery charger, reel time clock (RTC), CMOS, and lithium batteries are among its various sections. The module's microprocessor processes the lightning detector's digital outputs, which are then recorded on a data logger for processing and data interpretation. The gadget was set up, and throughout the two months of April and May 2025, lightning data was gathered. The number of occurrences in connection to the number of days was examined using a regression model. The data was validated using the regression coefficient R2. The degree of fitness findings for linear, quadratic, and cubic, respectively, are 0.968, 0.971, and 0.971 for April and 0.979, 0.980, and 0.983 for May. The aforementioned results are well-fitting models that can be used to estimate the frequency of lightning strikes. The detector's memory, low power consumption, small size, mobility, and affordability are just a few of its numerous benefits.},
keywords = {Lightning Detector, Occurrences, Regression Model, Wi-Fi Module},
month = {August},
}