Detection of Illegal Cryptocurrency Mining Farms in Distribution Systems Using Harmonic State Estimation

Abstract

In recent years, the mining of cryptocurrencies has raised concerns due to its high profitability and secure environment, especially in countries with low energy prices. These concerns have resulted in a range of problems, including increased network load, electricity theft, harmonic distortion in the network, and a degradation in power quality, as well as an inaccurate estimation of load behaviors. This article presents a new approach that utilizes the concept of harmonic state estimation and unique characteristics of mining loads to identify unauthorized mining farms at the distribution network levels. Since miners are based on electronic power switching devices, they are recognized as harmonic-polluting loads. Using measurements and harmonic state estimation, it becomes possible to identify the potential locations of these loads. The proposed approach was implemented in DigSILENT software and tested on an 18-bus IEEE network. The results demonstrate the effectiveness of the proposed method in identifying the locations of harmonic loads from mining operations, detecting unauthorized energy consumption.