In many countries, huge power networks are formed from the interconnection of electric power companies each one having its own supervisory control center. In order to supervise efficiently such power systems, hierarchical state estimation techniques have to be considered. This dissertation presents a new algorithm using hierarchical concepts to solve the dynamic state estimation problem for large-scale electric power systems operating under quasi-static conditions. A two-level calculation is performed to give a satisfactory solution from the computational point of view for the usual steps of the dynamic state estimation procedure: model parameter estimation, state prediction and state filtering. At the local level, conventional dynamic state estimation algorithms are carried out simultaneously and independently for all subsystems. The coordination of these local estimations is obtained at a national center for supervision and control, which is at the top of operational hierarchy in the interconnected electrical system. Numerical results show the performance of the proposed algorithm under different operational conditions: normal, sudden variation of states and occurrence of bad data.