Knowledge-assisted Differential Evolution Based Non-Contact Voltage Measurement for Multiconductor Systems in Smart Grid

Multiconductor systems, such as overhead lines and multicore cables, are widely used in smart grids for their reliable power transmission capabilities. However, measuring the voltage of individual conductors in these systems has been challenging despite their promising applications. This paper proposes a novel framework to address this issue by utilizing a low-cost electric field (EF) sensor array and a knowledge-assisted differential evolution algorithm. The framework involves designing an annular EF sensor array to capture EF information around the multiconductor systems while ensuring its anti-interference capabilities through grounding and balanced electrodes. Furthermore, an optimization problem is formulated to reflect the model accuracy, and a knowledge-assisted differential evolution algorithm is proposed to optimize the voltage values and conductor positions by leveraging knowledge about the conductor positions and EF distribution. The use of this knowledge is expected to generate promising initial solutions for accelerating the optimization process. The proposed framework is experimentally validated, and the results demonstrate the robustness and accuracy of the EF sensor array and algorithm in obtaining the voltage of individual conductors in multiconductor systems.