1992
DOI: 10.1109/59.141719
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Variable-structure facts controllers for power system transient stability

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Cited by 112 publications
(25 citation statements)
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“…In case of a fault in the power system, the braking resistor is temporarily inserted in parallel at the nearest end of the generator to absorb the excessive energy produced by the generator acceleration. There are studies examining the use of braking resistors jointly with other devices to increase its efficiency [4][5][6], and we think that there are possibilities of their use for achieving an even better transient stability. Some examples of practical applications of braking resistors can be found in the Bonneville Power Plant (Oregon) [1], Four Corner Power Plant of Public Service Company (Arizona) [1][2][3], and in Owase Mita and Atsumi power plants of Chubu Electric Power Co. (Japan).…”
Section: Introductionmentioning
confidence: 97%
“…In case of a fault in the power system, the braking resistor is temporarily inserted in parallel at the nearest end of the generator to absorb the excessive energy produced by the generator acceleration. There are studies examining the use of braking resistors jointly with other devices to increase its efficiency [4][5][6], and we think that there are possibilities of their use for achieving an even better transient stability. Some examples of practical applications of braking resistors can be found in the Bonneville Power Plant (Oregon) [1], Four Corner Power Plant of Public Service Company (Arizona) [1][2][3], and in Owase Mita and Atsumi power plants of Chubu Electric Power Co. (Japan).…”
Section: Introductionmentioning
confidence: 97%
“…DP computes the value function in order to find the optimal control with a feedback control policy. Indeed, from the value function the following optimal feedback control policy is deduced (5) Alternatively, one can define so-called function as (6) Then can be expressed as a function of (7) Equation (5) can be rewritten as (8) Equation (8) provides a straightforward way to determine the optimal control law from the knowledge of the . RL algorithms estimate the function by interacting with the system.…”
Section: A Theoretical Frameworkmentioning
confidence: 99%
“…RL algorithms estimate the function by interacting with the system. From the knowledge of the function, they can decide by using (8) which value of the control to associate to a state in order to maximize the discounted return (2). Unfortunately, RL in a continuous state-space implies that the function has to be approximated [18].…”
Section: A Theoretical Frameworkmentioning
confidence: 99%
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