Analysis of Measurement Errors in Smart Electricity Meters under High-Humidity, High-Heat, and High-Salt Fog Conditions

As a key device in smart grids, the metering accuracy of smart meters is in urgent need of in-depth research regarding its variation mechanisms under complex environmental stress. This paper takes the typical high-humidity and high-temperature environment in Fujian as the background and relies on the long-term operation data from the State Grid's high-humidity, high-temperature, and high-salt fog typical environment laboratory for smart measurement equipment to systematically conduct multi-stress coupling experiments and nonlinear regression modeling analysis. The experimental platform simulates a high-salt fog and high-humidity environment, combining multi-variable monitoring with current load control to collect metering error data from electricity meters. Pearson correlation analysis is employed to quantify the association between environmental stress and errors. The analysis results show that temperature and operating time are the dominant factors in the evolution of errors, followed by salt fog concentration, while air pressure exhibits a weak negative correlation. Moreover, a multiple regression model containing quadratic and cross terms is constructed, and the optimal model is selected based on mean square error, adjusted R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, and Akaike information criterion. The experiments indicate that the inclusion of quadratic terms significantly improves the model accuracy, while the contribution of cross terms is limited. This study reveals the dynamic patterns of smart meter errors in high-humidity and high-temperature environments, providing data support for environmental adaptability design and optimization of operation and maintenance strategies.