فهرست نشریات علمی دسترسی آزاد ایران

Cryptocurrency mining, propelled by its high profitability and the availability of low-cost energy in certain regions, presents significant challenges to distribution networks, including unauthorized energy consumption, increased network loading, power theft, harmonic distortions, and degraded power quality. This paper introduces an innovative approach based on Harmonic State Estimation (HSE), which leverages the unique harmonic signatures of mining loads to accurately detect unauthorized cryptocurrency mining operations within distribution networks. The developed methodology could successfully identify the miners even in the presence of other harmonic-distorted loads such as steel industries, smelting plants, and textile factories. By analyzing harmonic measurements, harmonic power output, and power factor across various distinct scenarios involving varied harmonic loads alongside unauthorized mining activities, the proposed method precisely locates these operations. Implemented and validated using DigSILENT PowerFactory on standard test networks, the results underscore the method’s accuracy in differentiating mining activities from other harmonic sources, offering distribution network operators a robust tool to mitigate power theft, enhance network stability, and improve overall reliability.