Document Type : Research paper


1 Department of Electrical Engineering, Sitamarhi Institute of Technology, Sitamarhi, Bihar, India

2 Department of Electrical Engineering, Rajkiya Engineering College, Mainpuri, Uttar Pradesh, India

3 Department of Electrical and Electronics Engineering, Nalanda College of Engineering, Chandi, Nalanda, Bihar, India

4 Department of Electrical Engineering, Government Engineering College, Siwan, Bihar, India

5 Department of Electrical and Electronics Engineering, Shershah Engineering College, Sasaram, Bihar, India

6 Department of Electrical and Electronics Engineering, Gaya College of Engineering, Gaya, Bihar, India

7 Department of Electrical Engineering, B.N.C.E.T., Lucknow, Uttar Pradesh, India


In the family of Flexible AC Transmission Systems (FACTS) controllers, the distributed power flow controller (DPFC) can control powerfully all the system's parameters like bus voltages magnitude, transmission angle, and line impedances with high redundancy and a wide range of compensation. In this paper, IEEE-14 bus IEEE-30 bus, and IEEE-118 bus systems are taken for the testing of the proposed approach. The optimal placement of the series and shunt converters of the DPFC is decided by the most critical bus and most critical line associated with that bus respectively. The sizing of the DPFC is decided based on the minimization of active power losses of the systems. The loss function is considered an objective function and the limits of the bus voltages magnitudes, bus voltage angles, thermal limits of the lines, and level of compensation of the DPFC are taken as the system's constraints. To solve complex problems in various fields, meta-heuristic optimizations are more popular. Among the meta-heuristic optimizers, the jellyfish optimizer is one that is based on the behavior of jellyfish in the ocean. The optimization of the objective function with constraints has been solved by time-varying acceleration coefficients (TVAC) particle swarm optimization (PSO), artificial bee colony (ABC), genetic algorithm (GA), and metaheuristic optimizer jellyfish methods. Results show that all the optimization techniques provide solutions with minimum losses. Among these methods, the solution of the jellyfish optimizer has the lowest active power losses, highest convergence rate, less number of iterations, and also takes less computational time.


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