Sliding mode control of permanent magnet synchronous motor fed by wind turbine generator taking saturation effect into account

Benchabane, Fateh; Titaouine, A.; Bennis, Ouafae; Guettaf, Abderrazak; Yahia, K.; Taibi, Djamel

doi:10.1109/acemp.2011.6490625

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…The control rules are designed to assign a fuzzy set of the control input for each combination of fuzzy sets of and [19,20]. Table 1 shows one of possible control rule base.…”

Section: Iv2 Fuzzy Logic Controllermentioning

confidence: 99%

“…The pump used is of centrifugal type which can be described by an aerodynamic load which is characterised by the following load equation [1,5]: 2 l TA  (19) where A is the pump constant.…”

Section: Pump Modelmentioning

confidence: 99%

“…The plant control u is inferred from the two state variables, error (e) and change in error e [19,20].…”

Section: Iv2 Fuzzy Logic Controllermentioning

confidence: 99%

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An improved efficiency of fuzzy sliding mode control of permanent magnet synchronous motor for wind turbine generator pumping system

et al. 2012

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Abstract-This paper presents an analysis by which the dynamic performances of a permanent magnet synchronous motor (PMSM) motor is controlled through a hysteresis current loop and an outer speed loop with different controllers. The dynamics of the wind turbine pumping drive system with (PI) and a fuzzy sliding mode (FSM) speed controllers are presented. In order to optimize the overall system efficiency, a maximum power point tracker is also used. Simulation is carried out by formatting the mathematical model for wind turbine generator, motor and pump load. The results for such complicated and nonlinear system, with fuzzy sliding mode speed controller show improvement in transient response of the PMSM drive over conventional PI. The effectiveness of the FSM controller is also demonstrated.

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“…The control rules are designed to assign a fuzzy set of the control input for each combination of fuzzy sets of and [19,20]. Table 1 shows one of possible control rule base.…”

Section: Iv2 Fuzzy Logic Controllermentioning

confidence: 99%

Section: Pump Modelmentioning

confidence: 99%