Multi-Sensor fusion and data analysis for operating conditions of low power transmission lines

The research of the safety distance among drones and cable attracted wide attention with the rising deployment of unmanned aerial vehicle (UAV) to check for high-voltage overhead power systems. To increase the inspection operation's dependability and guarantee a safe and stable functioning of the transmission grid and inspections equipment, it is essential to determine the safety distance among the UAV and the driveway. Due to UAV patrol safety distances from overhead power lines, it is difficult to offer precise surfing information because of the lack of quantitative assistance. The author of the relevant study uses multi-sensor fusion data analysis (MFDA-LPTL). There have been discussions on the properties of large data sets, smart networks, and gigantic data sets before the emergence of low-cost power transmission lines and the benefits they may give. For example, using the adaptive weighted fusional method, which combines first-level data on homogenous sensor input based on critical UAVinfluencing factors such as maximum inspection speed, wind speed, positioning error, and drone size, can help achieve this goal. As a secondary benefit, the theory makes use of more robust evidence than before. However, the use of big data analytics in present smart grids must be expanded due to numerous challenges, such as the need for new technologies and increased public awareness. The experimental analysis shows that the proposed MFDA-LPTL model increases the performance ratio of 98.9%, efficiency ratio of 97.2%, reduces the detection failure analysis of 10.2%, processing time of 7.8%, positioning error rate of 12.3% compared to other existing methods.