2018
DOI: 10.17775/cseejpes.2016.00510
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Integrated multi-stage LQR power oscillation damping FACTS controller

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Cited by 21 publications
(11 citation statements)
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“…The optimal placement of FACTS devices is a common yet important research topic in literature and has been investigated via various approaches. These include (1) conventional methods (such as indexing [11], controlling [12], residue analysis [13], numerical optimization [14], sensitivity [15], and eigenvalue [16]), (2) optimization methods (such as optimal power flow [17], linear programming [18], dynamic programming [19], mixed integer programming [20], stochastic load flow [21], and adaptive control law [22]), (3) artificial intelligence techniques (such as Monte Carlo simulation [23], artificial bee colony [24], artificial neural network [25], symbiotic organism search algorithm [26], fuzzy systems [27], and particle swarm optimization [28]), (4) hybrid techniques (such as hybrid of bee colony and neural networks [29], hybrid of genetic algorithm and fuzzy systems [30], mixed optimal power flow and particle swarm optimization [31], mixed bee colony and optimal power flow [32], and hybrid of fuzzy systems and Lyapunov theory [33]), and (5) other approaches (such as energy approach [34], active control [35], graph search algorithms [36], whale optimization [37], Gray Wolf optimizer [38], salp swarm optimizer [39], Grasshooper optimization [40], ant lion optimization [41], and spider monkey optimization [42]).…”
Section: A State Of the Artmentioning
confidence: 99%
“…The optimal placement of FACTS devices is a common yet important research topic in literature and has been investigated via various approaches. These include (1) conventional methods (such as indexing [11], controlling [12], residue analysis [13], numerical optimization [14], sensitivity [15], and eigenvalue [16]), (2) optimization methods (such as optimal power flow [17], linear programming [18], dynamic programming [19], mixed integer programming [20], stochastic load flow [21], and adaptive control law [22]), (3) artificial intelligence techniques (such as Monte Carlo simulation [23], artificial bee colony [24], artificial neural network [25], symbiotic organism search algorithm [26], fuzzy systems [27], and particle swarm optimization [28]), (4) hybrid techniques (such as hybrid of bee colony and neural networks [29], hybrid of genetic algorithm and fuzzy systems [30], mixed optimal power flow and particle swarm optimization [31], mixed bee colony and optimal power flow [32], and hybrid of fuzzy systems and Lyapunov theory [33]), and (5) other approaches (such as energy approach [34], active control [35], graph search algorithms [36], whale optimization [37], Gray Wolf optimizer [38], salp swarm optimizer [39], Grasshooper optimization [40], ant lion optimization [41], and spider monkey optimization [42]).…”
Section: A State Of the Artmentioning
confidence: 99%
“…[2][3][4][5][6] However, the PSS usually is effective for the local oscillation modes, while little effect can be achieved when PSS is faced to the inter-area oscillations. To overcome the shortage of PSS and damp the interarea LFO effectively, grid-side LFO controllers are proposed for the development of flexible ac transmission system (FACTS) technologies, such as using STATCOM through particle swarm optimization technique to damping oscillations, 7 designing multiple FACTS supplementary damping controllers based on the static var compensation (SVC), thyristor controlled series capacitor (TCSC), 8 and so on.…”
Section: Introductionmentioning
confidence: 99%
“…Tuning methods of WAPSS includes residue-based method [6,7], pole placement method [8][9][10], robust control [11,12] etc. Design of WAC based on linear quadratic regulator (LQR) or linear quadratic Gaussian (LQG) [13][14][15][16][17][18][19][20][21][22][23] controllers utilise state estimators to determine the dynamic states of the system. The fuzzy logic controllers [24,25] utilise the output signals, which have maximum observability on the inter-area modes and utilise a fuzzy rule to generate the required control signal.…”
Section: Introductionmentioning
confidence: 99%