The mathematical modeling of two area network with interconnected thermal power systems has been done on the state space and an optimal control system technique known as Linear Quadratic Regulator (LQR) along with proportional integral (PI) controller is designed for the frequency response enhancement of the system in this paper. The PI controller gains are taken as the optimal state-feedback gains along with other state variables of the system for automatic generation control(AGC).The reheat turbine for thermal areas have been considered for the system. When the load on the system changes the variation in the frequency should be minimum for a system with properly designed automatic generation control. To enhance the frequency response against the load changes, the optimal controller known as Linear Quadratic Regulator (LQR) is tuned by powerful computationally intelligent technique Particle Swarm Optimization (PSO) . In the present work first the block diagram of the two area interconnected thermal power system is designed. Then the state equations for the power system are obtained for the different states in the block diagram to develop the state space model. The proposed PSO based LQR technique for automatic load frequency control of two area thermal power system has been developed by using MATLAB/SIMULINK.

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Abstract-The system comprises of three interconnected power system networks based on thermal, wind and hydro power generation. The load variation in any one of the network results in frequency deviation in all the connected systems .The PI controllers have been connected separately with each system for the frequency control and the gains (Kp and Ki ) of all the controllers have been optimized along with frequency bias (Bi) and speed regulation parameter ( Ri ) . The computationally intelligent techniques like bacterial foraging optimization (BFO) and particle swarm optimization (PSO) have been applied for the tuning of controller gains along with variable parameters Bi and Ri. The gradient descent (GD) based conventional method has also been applied for optimizing the parameters Kp , Ki ,Bi and Ri .The frequency responses are obtained with all the methods . The performance index chosen is the integral square error (ISE). The settling time, peak overshoot and peak undershoot of all the frequency responses on applying three optimization techniques have been compared. It has been observed that the peak overshoot and peak undershoot significantly reduce with BFO technique followed by the PSO and GD techniques. While obtaining such optimum response the settling time is increased marginally with bacterial foraging technique due to large number of mathematical equations used for the computation in BFO. The comparison of frequency response using three techniques show the superiority of BFO over the PSO and GD techniques. The designing of the system and tuning of the parameters with three techniques has been done in MATLAB/SIMULINK environment.Keyword -Bacterial foraging optimization; Particle swarm optimization; Gradient Descent ; Integral square error; peak overshoot; peak undershoot; settling time; PI controller I. INTRODUCTION One of the power system requirement is the stable operation in respect of voltage and frequency under varying load conditions. The variation in these parameters must lie within permissible range. The real power control help in achieving the frequency control and reactive power help in achieving the voltage control. The frequency deviation occurs in all the interconnected areas when they are connected through same tie line. The performance index has to be minimized continuously whenever there is frequency change .The performance index in the present case is chosen as the integral square error (ISE) which has to be minimized whenever there is frequency change using the computationally intelligent techniques. The load frequency controller is the proportional integral (PI) controller which is the most effective controller if it is properly tuned for proportional (Kp) and integral (Ki) gains and it is used in decentralized control mode as the interconnected areas are of unequal nature. The interconnected power system areas in most of the literature are of same nature but in actual scenario this trend is changing due to the increasing scarcity of non renewable energy generation sources. The interconn...

This paper presents an efficient hybrid approach-based energy management strategy for grid-connected microgrid (MG) system. The proposed hybrid technique is the combination of both random forest (RF) and cuttlefish algorithm (CFA) named as RFCFA. The proposed hybrid technique is utilized to decrease the electricity cost and increase the power flow between the source and load side. The MG system is tracked by the RF technique. The CFA is optimized based on the MG with the predicted load demand. MG employs two energy management strategies to reduce the impact of renewable energy prediction errors. The first strategy seeks at minimizing electricity costs during MG's operation. And the second strategy is aimed at balancing the power flow and reducing forecast error effects. In the grid-connected MG system, the objective function of the proposed technique is characterized with the inclusion of fuel cost, grid power variation, operation and maintenance cost. Battery energy storage systems (BESSs) can stabilize the output power and allow renewable power system units to operate at stable rate of output power. The proposed hybrid technique is executed in the working platform of MATLAB/Simulink, and the execution is evaluated using existing techniques such as GA, CFA and RBFNBBMO.

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