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Hydro power plants are in general more stable to the load demands and thus the change in frequency can be easily controlled. Whereas in multi-source interconnected power system the stability of the system is marginal. In certain conditions the system is also unmeasurable to implement the load frequency control. Linear Quadratic Regulator with output feedback design forms a familiar optimal choice to tackle this problem, but the technique is dependent on the choice of the weighting matrices Q and R for the performance index.Particle swarm optimization technique is commonly used in power system applications for obtaining the weighting matrices. In this paper, an output feedback-based LQR-PID technique is used to obtain an improved response for the multisource systems. Due to integral action, the steady-state value achieved for the change in frequency is minimized. In addition, if the system is unmeasurable LQR technique cannot be applied, in the literature, Linear Quadratic Gaussian is used for the same. Here a balanced truncation model order reduction is used on the original system to convert it to a measurable system.In addition, a comparative analysis is included to highlight the efficacy of the proposed control technique.

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Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise.