مدونة د . ميلاس عبدالصمد


مقال علمي صنف A حول طاقة الرياح منشور في مجلة Energy reports

ميلاس عبدالصمد | MILLES ABDESSMAD


21/01/2025 القراءات: 8  


الملخص:
(IT2-FLC) for a dual star induction generator (DSIG) based wind turbine system. The proposed study uses var-iable wind speed to generate the mechanical energy needed to operate a 1.5 MW DSIG-based wind turbine system. While traditional type 1 fuzzy logic controllers (T1-FLC) have been successful in various applications, they struggle with high uncertainty in complex, nonlinear systems. In contrast, the IT2-FLC technique provides improved capabilities to effectively manage uncertainty and unreliability of information. To address this, the IT2- FLC technique has been developed due to its superior uncertainty management, where the Gaussian IT2 fuzzy sets are employed in this paper due to their concise representation and simultaneous capability to capture suf-ficient uncertainties. The utilization of type 2 fuzzy membership functions in the controller ensures robust performance and eliminates the necessity for a model-based adaptive control scheme. The proposed IT2-FLC strategy for DSIG was implemented in a Matlab environment, where variable wind speed was used for this purpose. Performance comparisons were made between an IT2-FLC and a T1-FLC technique, revealing the robustness of the latter in terms of track references, rise time, total harmonic distortion (THD) of current, and overshoot. The results prove that the IT2-FLC strategy reduced the THD value of current by rates estimated at 14.72% and 25.88% in the two tests compared to the T1-FLC strategy. Also, the overshoot value was reduced by an estimated ratio of 9.21% in the first test compared to the T1-FLC strategy. The IT2-FLC strategy reduced the rise time value by rates estimated at 4% and 9.75% compared to the T1-FLC strategy. These ratios show the satisfactory performance of the IT2-FLC strategy in improving the properties of DSIG.
رابط المقال:
http://dx.doi.org/10.1016/j.egyr.2024.01.060


Wind energy DSIG Fuzzy logic type 2


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