A new dynamic data reconciliation method based on constrained strong tracking filter was proposed.Strong tracking filter was used for dynamic data reconciliation

while algebraic constraints were embedded in strong tracking filter

not only to overcome the shortcomings of traditional extended Kalman filter which loses tracking ability for mutant state when the system reaches a steady state and cannot deal with algebraic constraints

but also to save the iterative nature of the algorithm and greatly reduce computation time of the dynamic data reconciliation with algebraic constraints.An adaptive strategy was introduced for dynamic data reconciliation of largescale systems

which further reduced the computing time of reconciliation.Two simulations of examples proved the efficiency and superiority of the proposed algorithm.