Enhancing low-cost sensor performance for THD monitoring in smart meters using AI algorithms.

This research addresses the challenge of accurately measuring total harmonic distortion (THD) in smart meters, where traditional methods, such as FFT-based estimation, often fail to maintain high accuracy, especially when using low-cost sensors. The primary objective is to evaluate the potential of artificial intelligence (AI) techniques to improve THD estimation despite the limitations of economic sensor technology. To achieve this, the study employs a range of AI models, including AdaBoost, Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN), utilizing data initially gathered from high-resolution sensors, which provide accurate reference measurements. Lower-resolution data were then extracted to simulate the performance of economic sensors, which typically offer less precise measurements. Among the AI models tested, AdaBoost consistently outperformed the others in terms of accuracy. In contrast, traditional FFT methods exhibited substantial performance degradation, particularly with low-cost sensors. By applying artificial intelligence, a trained model was developed, where inputs from economic sensors are processed to yield outputs that closely approximate those of high-precision sensors. The results confirm that integrating AI algorithms, especially AdaBoost, improves the performance of low-cost sensors, enabling them to achieve results comparable to high-precision, expensive sensors. This breakthrough not only provides a scalable and cost-effective solution for power quality analysis but also facilitates the development of affordable, high-quality sensors that can be deployed in smart meters without compromising measurement accuracy. The results were also experimentally confirmed in real-world scenarios, further validating the effectiveness of the AI-enhanced approach.