A Particle Swarm Optimization Method for Power System Dynamic Security Control

Development approach of a programmable and open software package for power system frequency response calculation

“…There are many algorithms to deal with model parameter equivalence, such as particle swarm optimization method (PSO) [27], dynamic aggregation [28,29], weighted summation method and least square method [30,31]. Appropriate algorithms can be implemented for desired applications.…”

Preventive Control for Transient Security with Generation Rescheduling Based on Rotor Trajectory Index

“…If there is no relative rotor angles violations of any generator with respect to COI, Δδ i,COI (≤ δ max ) after fault clearing, the system is said to be secure (1), else insecure (0). For this study δ max is taken as 120° ( [22,23]). …”

“…Various generator rescheduling techniques [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] have been proposed for transient security control. In [6], the transient energy function (TEF) method is used to check the dynamic security of the system and the coherent behavior of generators is used to find a new generation configuration.…”

“…Decision tree (DT)-based preventive and corrective control methods are proposed in [21] to enhance the dynamic security of power systems against the credible contingencies causing transient instabilities. A radial basis function (RBF) neural network to assess the dynamic security status of the power system and to estimate the effect of a corrective control action applied in the event of a disturbance is proposed in [22] with optimal control action using particle swarm optimization (PSO). In [23], an optimal power flow model with transient stability constraints is proposed for both dispatching and re-dispatching.…”

Application of Statistical Methods for Gas Turbine Plant Operation Monitoring

“…In this chapter, the first method discussed in Section 2.1 is machine learning techniques which use a logical induction process to categorise a series of examples, resulting in decision tree and rules set which can be implemented in decision making processes. Typical application areas are fault diagnosis in industria1 machines (Michalski et a1., 1999) and the assessment of power system security (Voumvoulakis, 2010) Case based reasoning methods as discussed in Section 2.2, are commonly applied to decision making tasks where previous experience is desirable, but may not be available. Case based reasoning provides an inexperienced user with exposure to experiences from others, through a set of historical 'cases', and has been of particular use in areas such as fault diagnosis (Wang et al , 2008;Yan et al , 2007) and system design and planning (Hinkle and Toomey, 1995).…”