Int. J. of Business Intelligence and Data Mining   »   2018 Vol.13, No.1/2/3

 

 

Title: UPFC damping controller design using multi-objective evolutionary algorithms

 

Authors: G. Kannayeram; P.S. Manoharan; M. Willjuice Iruthayarajan; T. Sivakumar

 

Addresses:
Department of Electrical and Electronics Engineering, National Engineering College, K.R. Nagar, India
Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India
Department of Electrical and Electronics Engineering, National Engineering College, K.R. Nagar, India
Department of Electrical and Electronics Engineering, National Engineering College, K.R. Nagar, India

 

Abstract: In this paper, modified non-dominated sorting genetic algorithm-II (MNSGA-II)-based optimal damping control of unified power flow controller (UPFC) has been designed to enhance the damping of low frequency oscillations in power systems. The robust damping of UPFC controller design is formulated as a multi-objective optimisation problem, thereby minimising the integral squared error (ISE) of speed deviation and input control signal (u) under a wide range of operating conditions. The effectiveness of the proposed controller is confirmed through nonlinear time domain simulation and Eigen value analysis. The results are compared with NSGA-II and conventional method. Simulation result reveals that the obtained Pareto-front using MNSGA-II-based UPFC controllers are better and uniformly distributed due to the controlled elitism and dynamic crowding distance concepts. The proposed modulation index of shunt inverter (mE)-based damping controller is superior to the other damping controllers under different loading conditions and improves the stability of system.

 

Keywords: flexible AC transmission systems; FACTS; unified power flow controller; UPFC; non-dominated sorting genetic algorithm; NSGA-II; modified NSGA-II; MNSGA-II; integral squared error; ISE; genetic algorithmgravitational search algorithm; GA-GSA.

 

DOI: 10.1504/IJBIDM.2017.10003632

 

Int. J. of Business Intelligence and Data Mining, 2018 Vol.13, No.1/2/3, pp.52 - 74

 

Available online: 07 Dec 2017

 

 

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