Research on planning and evaluation of self recovery ability of intelligent power gridafter fault based on big data of power grid

Theself recovery research of smart grids is of great significance. Currently, the self recovery ability of power grids based on big data has not received sufficient attention and evaluation. The existing self recovery ability evaluation methods for intelligent power grid have various problems, mainly including incomplete quantitative indicators and neglect of uncertainty in theself recovery process. These problems combine to result in inaccurate evaluation, which is far from the actual results. In order to better solve the above problems, this article selects four quantitative indicators: self repair reliability, self repair rate, self repair speed, and self repair benefits to evaluate the self recovery ability of intelligent power grid. These four indicators cover the speed, sustainability, and economic benefits of load recovery after power gridfaults. On this basis, this article proposes a overall evaluation method forself recovery performance based on information entropy, and introduces uncertainty theory to quantitatively describe the uncertainty of self recovery, in order to balance the uncertainty and sample shortage issues in the self recovery evaluation process. Finally, the effectiveness and accuracy of the evaluation indicators and methods proposed in this article were experimentally verified.