Nowadays, smart power systems have undergone many changes due to economic issues and the application of novel technologies, among which are privatization and topics related to the electricity market. The effect of such changes in the structure of the power system is the introduction of new topics such as management, control, and monitoring of smart power systems, which require having accurate information on the values of current and voltage (or active and reactive power) at different points of the system. State estimation is a suitable technique for accessing smart grid information, which is based on the mathematical relationship between system state variables and measurements obtained from the system. If the state estimation relations of a system can be solved, that system is observable. If there is a measuring device in all the buses of a smart power system, it is completely observable, but this (having a meter in all buses of a system) is economically unacceptable. Therefore, it is necessary to determine suitable places for measurement for each system so that the estimation equations can be solved. In various articles, allocation of phasor measurement units (PMUs) has been carried out for state estimation in power system, but due to the smartness of power networks, the use of such equipment has been welcomed. Therefore, this study deals with the allocation of network parameters measurement tools in the form of phasors, considering the effect of switching substations in the grid and providing appropriate reliability. So far, many methods, including mathematics and intelligent methods, have been proposed for the allocation of PMUs. Obtaining accurate results and high convergence speed are the two main bottlenecks of the methods used in this regard. This research adopts classic particle swarm optimization (PSO) algorithms and a more advanced version of it that has high convergence and speed called time-varying acceleration coefficients PSO (TVACPSO) to place these devices.
