This　paper　proposes　the　implementation　of　dynamic　state　estimation　of　Doubly　Fed　Induction　Generator　(DFIG)　using　Cubature　Kalman　Filter　(CKF)　and　Square-root　Cubature　Kalman　Filter　(SRCKF).　First,　the　related　research　of　DFIG　dynamic　state　estimation　is　reviewed,　and　then　the　shortcomings　of　Extended　Kalman　Filter　(EKF)　and　Unscented　Kalman　Filter　(UKF)　and　the　advantages　of　CKF　and　its　extended　algorithm　are　explained.　Then,　a　specific　DFIG　model　is　used　for　simulation　analysis.　The　simulation　results　compare　the　performance　of　UKF,　CKF　and　SRCKF.　It　shows　that　SRCKF　can　avoid　the　non-positive　definiteness　of　the　covariance　matrix　during　CKF　iteration,　and　has　better　filtering　effect.　©　2021　IEEE.