Fuzzy neural network based voltage stability evaluation of power systems with SVC

Bacteria Foraging Optimization Algorithm based SVC damping controller design for power system stability enhancement

“…An adaptive network based fuzzy inference system (ANFIS) for SVC is presented in [8] to improve the damping of power systems. A multi input, single output fuzzy neural network is developed in [9] for voltage stability evaluation of the power systems with SVC. A method of determining the location of a SVC to improve the stability of power system is illustrated in [10].…”

Static VAR Compensator Damping Controller Design Based on Flower Pollination Algorithm for a Multi-machine Power System

“…The design problems of SVCs have gained attention, so various control techniques have been discussed for SVC design, including the conventional proportional integral derivative (PID) controller [15], robust technique [16], pole placement [17], fuzzy logic control (FLC) [18][19][20], artificial neural network (ANN) [21,22], hybrid algorithm [23], model reference adaptive control [24], genetic algorithm (GA) approach [25], particle swarm optimization [26,27], imperialist competitive algorithm (ICA) [28], and bacteria foraging (BF) [29,30].…”

Different Methods of Accident Forecast Based on Real Data

“…It based on the integration of two complementary theories. Purpose of the integration is to compensate weaknesses of one theory with advantages of the other [18]. On the one hand, it enhances the model interpreting ability of the neural network by making use of the interpreting ratiocination ability of the fuzzy system.…”