Abstract—This paper presents a differential evolution algorithm to design a robust PI and PID controllers for Load Frequency Control (LFC) of nonlinear interconnected power systems considering the boiler dynamics, Governor Dead Band (GDB), Generation Rate Constraint (GRC). Differential evolution algorithm is employed to search for the optimal controller parameters. The proposed method easily copes of with nonlinear constraints. Further the proposed controller is simple, effective and can ensure the desirable overall system performance. The superiority of the proposed approach has been shown by comparing the results with published fuzzy logic controller for the same power systems. The comparison is done using various performance measures like overshoot, settling time and standard error criteria of frequency and tie-line power deviation following a 1% step load perturbation in hydro area. It is noticed that, the dynamic performance of proposed controller is better than fuzzy logic controller. Furthermore, it is also seen that the proposed system is robust and is not affected by change in the system parameters. Keywords—Automatic Generation control (AGC), Generation Rate Constraint (GRC), Governor Dead Band (GDB), Differential Evolution (DE) I. INTRODUCTION OAD frequency control (LFC) is an important issue in power system operation and control. Large power systems are divided into different control areas. All such areas are connected, to be called as an interconnected power system. Interconnected power system is used to increase reliable and uninterrupted power supply. Normally, interconnected thermal-thermal or hydro-thermal type systems are considered. Automatic generation Control (AGC) is used to maintain scheduled system frequency and tie line power deviations in normal operation and small perturbation. AGC function can be viewed as a supervisory control function which attempts to match the generation trend within an area to the trend of the randomly changing load of the area, so as to keep the system frequency and the tie-line power flow close to scheduled value. The growth in size and complexity of electric power systems along with increase in power demand has necessitated the use of intelligent systems that combine knowledge, techniques and methodologies from various sources for the real-time control of power systems. Kothari et B.Mohanty is with the Electrical Engineering Department Veer Surendra Sai University of Technology, Burla (phone: 91-9437152730; e-mail: banaja_m@yahoo.com). P.K. Hota is Professor with Electrical Engineering Department Veer Surendra Sai University of Technology, Burla (e-mail: p_hota@rediffmail.com). al. [1] are possibly the first to consider Generation Rate Constraint (GRC) to investigate the AGC problem of a hydrothermal system with conventional integral controllers. Many a research has been done in AGC in two area thermal - hydro systems with non-linearity as GRC [2], [3]. In [4] Governor Dead Band (GDB) is considered as non-linearity, and the AGC problem is solved by PI controller tuned with Craziness Particle Swarm Optimisation (CPSO). It is observed that, considerable research work is going on to propose better AGC systems based on modern control theory [5], neural network [6], fuzzy system theory [7], reinforcement learning [8] and ANFIS approach [9]. But, these advanced approaches are complicated and need familiarity of users to these techniques thus reducing their applicability. Alternatively, a classical Proportional Integral Derivative (PID) controller remain an engineer’s preferred choice due to its structural simplicity, reliability, and the favorable ratio between performances and cost. Additionally, it also offers simplified dynamic modeling, lower user-skill requirements, and minimal development effort, which are major issues of in engineering practice. In recent times, new artificial intelligence-based approaches have been proposed to optimize the PI/PID controller parameters for AGC system. In [10], several classical controllers structures such as Integral (I), Proportional Integral (PI), Integral Derivative (ID), PID and Integral Double Derivative (IDD) have been applied and their performance has been compared for an AGC system. Nanda et al. [3] have demonstrated that Bacterial Foraging Optimization Algorithm (BFOA) optimized controller provides better performance than GA based controllers and conventional controllers for an interconnected power system. E. S. Ali and S.M. Abd-Elazim [11] have reported that, proportional integral (PI) controllers tuned with the help of Bacterial Foraging Optimization Algorithm (BFOA), provides better performance as compared to that with GA based PI controller in two area non-reheat type thermal systems. In [12], a modified objective function using Integral of Time multiplied by Absolute value of Error (ITAE), damping ratio of dominant eigenvalues and settling time is proposed where the PI controller parameters are optimized employed Differential Evolution (DE) algorithm and the results are compared with BFOA and GA optimized ITAE based PI controller to show its superiority. B. Anand et al. [13] have reported conventional PI controller with fuzzy logic controller (FLC) for stabilizing the frequency oscillations of AGC with nonlinearities. Load Frequency Control of Nonlinear Interconnected Hydro-Thermal System Using Differential Evolution Technique Banaja Mohanty, Prakash Kumar Hota L World Academy of Science, Engineering and Technology International Journal of Electrical, Robotics, Electronics and Communications Engineering Vol:8 No:2, 2014 460 International Scholarly and Scientific Research & Innovation International Science Index Vol:8, No:2, 2014 waset.org/Publication/9998385