Abstarct: Power grid monitoring and control require measurement of various grid variables including voltage, current and active and reactive power of busses and grid lines. Usually, the number of measuring devices in the system is large, and these devices have different errors and may not even send correct information. Therefore, there is a need to verify and use all the information to diagnose the network condition. As the first program in electricity dispatching centers, state estimation plays an important role in verifying and correcting information. The large interconnected system, are managed by several operators. In this situation, an integrated state estimation may not possible due to the huge amount of information and time-consuming calculations, as well as the confidentiality of the information. In this case, the use of distributed state estimation is useful and even unavoidable due to some advantages, such as high computational efficiency and parallel computing. This study investigates a multi-area distributed state estimation algorithm for large-scale interconnected power systems. The entire power grid is decomposed into a group of estimated models, each of them can be solved locally using a regional estimator and coordinated with the neighboring region. The IEEE 118-bus test system, was selected as a sample network and the desired state estimation method was implemented on this network. In this case, the estimation of the network states has been compared between integrated and decentralized multi-area method. Also, the use of genetic algorithm as an effective method has been investigated. The results show that the estimation of the multi-aera state estimation in a decentralized manner will lead to acceptable results.