Research on transmission line planning through massive data mining based on historical survey designs

The growing demand for reliable and efficient power transmission has necessitated innovative solutions for transmission line planning. Despite advancements in energy systems, transmission line planning faces challenges related to optimizing routes and capacities while accommodating historical data variability and demand forecasts. The objective of the study is to utilize massive data mining techniques on historical survey designs to enhance transmission line planning, improving efficiency and reliability in the electrical grid while reducing costs and environmental impact. The study collects historical transmission data, including load forecasts, transmission line specifications, outage records, and demand patterns. The proposed study introduces an Intelligent Grasshopper Optimization Algorithm (IGOA) method for analyzing historical transmission data. This method identifies optimal transmission line placements and capacities, and enhancing decision-making by optimizing multiple objectives, including cost, reliability, and efficiency in power transmission planning. Simulations are conducted on the IEEE 118-bus system, testing various cases to ensure the robustness and efficiency of IGOA approach. The outcomes demonstrate the plans generated through the proposed IGOA strategy yield significantly lower expansion costs compared to traditional transmission line planning models, exhibiting minimal operational infeasibilities that can be easily addressed in short-term expansion planning. This research highlights a robust framework that can be adapted to various energy systems, ultimately supporting more sustainable and reliable power transmission infrastructure.