Addressing anomalies in smart grids: Methods for detecting and localizing high-current loads

The concept of a smart grid encompasses a wide range of advanced technologies that surpass the capabilities of traditional power grids, enabling enhanced monitoring, control and efficiency. In this context, the term “anomaly” denotes unusual or unexpected occurrences, such as abnormal consumption patterns, infrastructure failures, power outages, cyber attacks, or energy theft. Anomaly detection is a critical aspect of improving the reliability and operational efficiency of Smart grid systems. In this study, we focus on detecting high current loads as anomalies in low voltage networks and present a system designed for this purpose. The system architecture includes distribution transformers and customer meters equipped with current sensors, radio frequency (RF) modems for wireless data transmission, and a central management server for data analysis and storage. The system employs machine learning (ML) algorithms for real-time anomaly detection and localization. Therefore, in the feature extraction phase, three distinct methods are utilized: Pattern Analysis, Discrete Wavelet Transform (DWT), and Fast Walsh–Hadamard Transform (FHWT). The Pattern Analysis method achieved the best performance using the Matern Gaussian Process Regression (MGPR) approach, whereas the DWT and FHWT methods yielded the most accurate results with the Optimizable Boosting Method. The results of the Pattern Analysis method indicate a test root mean square error (RMSE) of 69.36, a coefficient of determination (R²) of 0.97, and a mean absolute error (MAE) of 45.40. The DWT method, employing a five-level wavelet transform with Daubechies 18 as the main wavelet, achieved a test RMSE of 53.44, an R² of 0.98, and an MAE of 37.25. The FHWT method resulted in a test RMSE of 83.99, an R² of 0.95, and an MAE of 53.71. Among these methods, the DWT method demonstrated the highest accuracy, with an average error rate of 3.02%, while the Pattern Analysis method and the FHWT method exhibited error rates of 4.09% and 4.86%, respectively. This innovative anomaly detection and localization approach provides a robust foundation for future research and development in power distribution networks, with the potential to reduce energy losses and improve grid stability. This system holds significant promise for reducing energy losses, preventing equipment damage, and enhancing grid stability by enabling rapid identification of unauthorized consumption and fault conditions. Its real-time monitoring capability empowers utilities to mitigate risks associated with energy theft and transient overloads, directly contributing to operational cost savings and improved service reliability.