A rule-based fuzzy logic controller for a PWM inverter in a stand alone wind energy conversion scheme

Hilloowala, R.M.; Sharaf, A.Μ.

doi:10.1109/ias.1993.299150

Cited by 27 publications

(26 citation statements)

References 10 publications

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“…The controller has two inputs (the rotor speed and generator output power) and one output (the estimated maximum power). In similar way, a fuzzy controller is used to track the maximum power point in [46], but in this case is for a squirrel cage induction generator. Maximum power output of the system at different wind velocity is computed using a polynomial curve fit of third order.…”

Section: Power Signal Feedback (Psf)mentioning

confidence: 99%

Overall Description of Wind Power

Lemus

Serna

2014

ing.cienc.

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Section: Power Signal Feedback (Psf)mentioning

confidence: 99%

Overall Description of Wind Power

Lemus

Serna

2014

ing.cienc.

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“…A text block of FIS is shown in Fig.6. Four components of a fuzzy controller are fuzzifier, rule base, inference engine and defuzzifier [13] [14] [15].…”

Section: Fuzzy Controller Designmentioning

confidence: 99%

“…The fuzzy rules can be represented by a fuzzy associative memory (FAM) table as represented in Table 4 [15]. The fuzzy rules are developed for the fuzzy controller which produces appropriate speed indication to run the cane carrier motor depending upon the height of cane fiber in Donnelly chute and weight of cane in cane carrier.…”

Section: Fuzzy Controller Designmentioning

confidence: 99%

Design Algorithm and Performance Analysis of Conventional and Fuzzy Controller for Maintaining the Cane Level during Sugar Making Process

Misra¹,

Kamath²

2014

IJISA

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-The amount of cane fiber carried by cane carrier varies due to non-uniformity of cane supply. The continuous variation of cane in chute during the cane juice extraction inversely affects the cane juice extraction efficiency of mill. This paper deals with the development and performance comparison of conventional controller and fuzzy controller to maintain the cane level in chute during juice extraction. The conventional controller is developed using VHDL language and simulated by using Xilinx ISE 14.5 version. The fuzzy controller is developed and simulated by using fuzzy toolbox of 'MATLAB ® version 7.11.0.584 (R2020b).

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“…In [20], fuzzy logic controller is used to track the maximum power point. The method uses wind speed as the input in order to generate reference power signal.…”

Section: Power Signal Feedbackmentioning

confidence: 99%