The study presents the development of an improved information-measuring system for monitoring atmospheric electrostatic field strength, designed for integration into Internet of Things frameworks for environmental monitoring and anomaly detection. Conventional instruments for electrostatic field measurements are primarily oriented toward high field levels and demonstrate significant errors in the range below 1 kV/m, which restricts their applicability for environmental monitoring tasks. Additional limitations include low noise immunity, large dimensions, and the lack of compatibility with modern digital data acquisition systems. The proposed configuration of the Electrostatic Field Mill enables measurements of atmospheric electrostatic field strength with a resolution of 1–2 V/m and a relative error of less than 1%. The paper introduces the generalized structure of the improved information-measuring system and highlights its functional advantages. The core idea of the study is the integration of the electrostatic field mill as a sensor element into IoT-based systems to establish spatially distributed monitoring networks. The proposed framework includes wireless data transmission, data aggregation, and subsequent visualization through geographic information platforms. This approach lays the foundation for distributed sensor networks capable of providing early detection of atmospheric and geophysical anomalies, as well as monitoring electrostatic risks in industrial environments.
Anisimov, S. V., Galichenko, S. V., & Aphinogenov, K. V. (2018). Evaluation of the atmospheric boundary-layer electrical variability. Boundary-Layer Meteorology, 167(2), 327–348. https://doi.org/10.1007/s10546-017-0330-3
Antunes de Sá, A., Marshall, R., Sousa, A., Viets, A., & Deierling, W. (2020). An array of low-cost, high-speed, autonomous electric field mills for thunderstorm research. Earth and Space Science, 7(11), e2020EA001309. https://doi.org/10.1029/2020EA001309
Cui, Y., Yuan, H., Song, X., Zhao, L., Liu, Y., & Lin, L. (2016). Model, design and testing of field mill sensors for measuring electric fields under high-voltage direct current power lines. IEEE Transactions on Industrial Electronics, 65(1), 608–615. https://doi.org/10.1109/TIE.2017.2681982
Harrison, R. G., & Nicoll, K. A. (2018). Fair weather criteria for atmospheric electricity measurements. Journal of Atmospheric and Solar-Terrestrial Physics, 179, 239–250. https://doi.org/10.1016/j.jastp.2018.07.008
Liu, C., Williams, E. R., Zipser, E. J., & Burns, G. (2010). Diurnal variations of global thunderstorms and electrified shower clouds and their contribution to the global electrical circuit. Journal of the Atmospheric Sciences, 67(2), 309–323. https://doi.org/10.1175/2009JAS3248.1
Markson, R. (2007). The global circuit intensity: Its measurement and variation over the last 50 years. Bulletin of the American Meteorological Society, 88(2), 223–242. https://doi.org/10.1175/BAMS-88-2-223
Povschenko, O., & Bazhenov, V. (2023). Analysis of modern atmospheric electrostatic field measuring instruments and methods. Technology Audit and Production Reserves, 4(1(72)), 16–24. https://doi.org/10.15587/2706-5448.2023.281478
Povshenko, O., Bazhenov, V., Pazdrii, O., & Bohdan, H. (2023). Increasing the accuracy of electrostatic fields strength measurement by using an improved differential transimpedance amplifier circuit. Eastern-European Journal of Enterprise Technologies, 6(5(126)), 6–14. https://doi.org/10.15587/1729-4061.2023.292691
Seng, K. P., Ang, L. M., & Ngharamike, E. (2022). Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks. International Journal of Distributed Sensor Networks, 18(3), 1–14. https://doi.org/10.1177/15501477211062835
Su, J. J., & Chen, H. (2023). Study on the mechanism of atmospheric electric field anomalies before earthquakes. Results in Geophysical Sciences, 15, 100060. https://doi.org/10.1016/j.ringps.2023.100060
Wilson, J. G., & Cummins, K. L. (2021). Thunderstorm and fair-weather quasi-static electric fields over land and ocean. Atmospheric Research, 257, 105618. https://doi.org/10.1016/j.atmosres.2021.105618
Yamashita, K., Fujisaka, H., Iwasaki, H., Kanno, K., & Hayakawa, M. (2022). A new electric field mill network to estimate temporal variation of simplified charge model in an isolated thundercloud. Sensors, 22(5), 1884. https://doi.org/10.3390/s22051884