Fine Planning Method of Power Grid Based on Principal Component Analysis and Index Correlation Degree

With the power grid development changing from high-speed to high-quality stage, the proportion of new energy increasing and the scale of power grid operation data expanding, the planning and the decision-making is becoming more and more difficult. Moreover, it is hard to deal with the big data during the high-quality development stage of power grid and the inaccuracy of grid planning brings difficulties to decision making of grid construction and investment, though the traditional grid planning method can still handle the scientific requirement of decision-making model. This paper is mainly focused on the relevance between statistical characteristics of data indicators and power grid planning. The principal component analysis method is used to reduce the dimension of data indicators, which makes the data system more efficient. Meanwhile, the project is sorted by index correlation degree method, so that the planning point is more accurate and the refinement degree of grid planning is improved.