A new optimal unified power flow controller placement and load shedding coordination approach using the Hybrid Imperialist Competitive Algorithm-Pattern Search method for voltage collapse prevention in power system

Unified power flow controllers in smart power systems: models, methods, and future research

“…An exhaustive search in combination with nonlinear programming finds the optimal number, location, and parameter settings of UPFCs [131]. [57] June 2001 single location + settings one load level single min power loss practical heuristic [78] June 2004 single location one load level single max voltage stability practical heuristic [92] September 2005 single location one load level single max voltage stability practical heuristic [93] February 2006 single location + settings one load level single max social welfare practical heuristic [99] February 2007 single location one load level single min operational cost practical heuristic [117] March 2009 single location + settings three load levels single min generation cost practical heuristic [124] July 2010 single location + size multi-load level single min congestion cost practical heuristic [131] February 2011 multiple number + location + settings one load level single min operational cost nonlinear programming [140] July 2012 single location + size + settings multi-load level single min total system cost PSO [144] October 2012 multiple number + location + size + settings one load level single max social welfare exhaustive search [148] March 2013 single location + settings one load level single max damping ratio genetic algorithm [165] November 2014 single location + size one load level single max loading margin practical heuristic [167] January 2005 single location + size one load level single min power loss hybrid heuristic [182] July…”

“…An artificial bee colony algorithm finds the optimum location and gravitational search algorithm finds the optimum size of a UPFC [167]. A hybrid imperialist competitive algorithm and pattern search method finds the UPFC location and the minimum load shedding to prevent voltage collapse [182].…”

Optimal line flows based on voltage profile, power loss, cost and conductor temperature using coordinated controlled UPFC

“…Further complicated models, like co-ordination of multiple types of FACTS devices in the presence of reactive sources are solved using gravitational search algorithm in [30], and the efficiency of the chaotic krill herd algorithm is established in [31] to solve the OPFbased DC-link placement problem. A UPFC-based power system stabiliser is heuristically tuned using genetic algorithm (GA) for maximising damping ratios in [32], whereas the UPFC is used to prevent voltage collapse during contingency events by using hybrid imperialist competitive algorithm-pattern search in [33]. In the above study, it is noted that most of the techniques are modified for improving their suitability for the problem pattern.…”

Global continuous robust finite‐time control design for unified power flow controller

“…The UPFC is also able to improve transient stability and oscillation damping of power system . Therefore, the research on the advanced controller of UPFC has attracted the attention of many researchers …”

“…[1][2][3][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] In the past decade, many advanced controllers of the UPFC have been designed to compensate for a change of operating condition so that they are able to provide satisfactory performance over a wide range of operating conditions. [6][7][8][9][10][11][12][13][14][15][16][17][18] In order to improve the transient stability and damp low-frequency interarea oscillations of the power system, many nonlinear control strategies and intelligent control strategies of the UPFC have been proposed, such as direct Lyapunov function method, 1,6,7 nonlinear predictive control method, 3 feedback linearization control approach, 8,9 pseudogeneralized Hamiltonian function method, 10 terminal sliding mode control method, 16,17 adaptive nonlinear control, 20 quantum particle swarm optimizer method, 2,4 neural network predictive control approach, …”