Abstarct: Water shortage crisis and new generation of environmentally friendly energy is the most important challenge in the world. One of the solution to this challenge is the construction of a dam and power plant. Many equipment of power plant and network are sensitive to frequency variation, the steady speed of the turbine is one of the necessities. The governor controls the turbine speed and the governor uses the PID controllers for this purpose. Turbine speed control system includes hydraulic, turbine, servo motor (to open and close the valve) and generator. Non-minimum phase, complexity, non- linear and uncertainty makes tuning the PID controllers is difficult. In this thesis the evolutionary algorithm (PSO and GA) is used to set these coefficients, these algorithms have had a good performance in optimization issues. The PSO algorithm performs better than the genetic algorithm. The problem of both algorithms is the slow convergence rate. To solve this problem, the PSO was used for optimization of neural network weights. Because the system model is not accurate, an identifier has been used to adjust the controller coefficients. Finally the Dez dam is modeled. The above algorithms are simulated to identify and control this dam. In simulating the controller’s performance for sudden change of load and sudden change of set point is done.