Knowledge-Assisted Approach for Contactless Current Measurement in Multiconductor Systems

Current measurements of multiconductor systems are essential for smart grids, but conventional intrusive measurement approaches require power outages before deployment and maintenance and thus are expensive and may reduce system stability. Though many contactless current measurement approaches have been proposed in recent years, the challenge of easing the impact of conductor positions and decoupling current information remains. This paper proposes a novel approach that combines a cost-effective annular magnetic field (MF) sensor array with an inverse calculation technique for precise, contactless current measurements in multiconductor systems. Specifically, the MF sensor array captures the MF distribution surrounding the multiconductor systems for characterizing the coupled current information. Then, the hardware-oriented measurement task is reformulated into a computational optimization problem, where the currents and conductor positions are mathematically related to the MF distribution for decoupling. Meanwhile, an effective algorithm is tailored, where knowledge about the current and MF distribution is used to generate promising initial solutions for efficiency and effectiveness improvement. Experimental results demonstrate that the proposed approach achieves high accuracy in current measurement considering the impact of conductor positions, with error rates maintained below 1% and 2% in balanced and unbalanced cases, respectively. Additionally, an abnormal MF sensing data correction method is developed to further ensure measurement accuracy, showing resilience to sensor anomalies and maintaining a relative measurement error below 2% after correction.