## 1 Introduction  
Load frequency control plays a substantial function in power system operations, so control engineers are attempting to use several control methods in order to obtain the best solutions [1,2]. A proportional integral (PI) controller or an only integral controller is still widely applied in load frequency applications.  
In the literature, there are many techniques that have been applied for the load frequency issue. From these techniques, fixed parameter controllers are developed at nominal operating points, and they, however, may not be suitable under various operating conditions. For this reason, adaptive gain scheduling approaches have been proposed for LFC synthesis [3]. With this method, the disadvantages of the conventional proportional integral and derivative (PID) controllers, which require the adaptation of controller parameters, were overcome. However, it faces some difficulties such as the instability of transient response because of abrupt changes in the system parameters, in addition to the impossibility of obtaining accurate linear time invariant models at variable operating points [3]. In addition to dealing with changes in system parameters, fuzzy logic controllers have been used in many reports for LFC design in a two-area power system [4,5]. The applications of artificial neural networks and genetic algorithms in LFC have been studied in [6,7]. In spite of these efforts, it seems that, although the estimation of parameters is not required, the parameters of the controllers can be generally changed very quickly; despite the promising results achieved, the control algorithms are complicated and unstable transient response could still be observed. Therefore, some other elegant techniques are needed to achieve a more desirable performance.  
Recently, some papers have reported the application of the model predictive control (MPC) technique on the load frequency control issue [8,9]. In [8], the use of MPC in a multi-area power system is discussed. In [9], the effect of merging wind turbines on the multi-area power system controlled by MPC is discussed. From [8,9], fast response and robustness against parameter uncertainties and load changes can be obtained using an MPC controller, but MPC suffers from a calculation burden problem [8,9].  
In [10], a robust technique using the coefficient diagram method (CDM) was presented for LFC. CDM is an algebraic algorithm which is applied to a polynomial loop in the parameter space using a coefficient diagram to get the necessary design information. Simulation results supported CDM as a power system load frequency controller.  
As a result of the complexity and change of the power system structure, new methods are asked to enhance the system performance.  
This paper presents a distributed LFC control method based on both the linear quadratic Gaussian (LQG) method and the CDM technique. LQG is designed to produce an optimal feedback control signal. System full states have been estimated using the Kalman filter technique. Both the Kalman state space model and the LQG feedback gains have been designed off-line. A system with the proposed CDM + LQG technique has been checked under parameter uncertainties and load disturbances using a digital simulation. A comparison between the proposed technique, CDM alone and the traditional integral controller are carried out, supporting the effectiveness of the proposed CDM + LQG technique.