Makine öğrenimi kullanarak ÇBAG'a dayalı rüzgâr türbininin FRT yeteneğinin iyileştirilmesi

Çift beslemeli asenkron generatörü (ÇBAG), şebeke arızası sırasında meydana gelen yüksek gerilimin ve akımın zararlı etkilerine karşı çok hassastır. Makine öğrenmesi (ML) yöntemlerinden biri olan destek vektör makineye (DVM) dayalı bir kapasitif köprü tipi arıza akım sınırlayıcısı (KKTAAS), üç fazlı arızada geçiş (FRT) performansını iyileştirmek için önerilmiştir. Bu çalışmada, normal şebeke koşullarında çalışan ÇBAG tabanlı bir rüzgâr türbininde oluşabilecek faz-toprak (3LG) simetrik şebeke hatası DVM' ye dayalı makine öğrenimi algoritması hem ÇBAG dönüştürücülerin kontrol sistemlerinde hem de KKTAAS' in bir kontrol sisteminde uygulanmıştır. Rotor tarafında, şebeke tarafında dönüştürücüde ve KKTAAS' in devre topolojisinde kullanılan elektronik anahtarlama elemanlarının anahtarlama sinyallerini üretmek için dört farklı DVM sınıflandırıcı algoritması uygulanmıştır. DVM sınıflandırıcılarının eğitiminde İnce Gauss, Kuadratik, Kübik ve Doğrusal kernel fonksiyonları tercih edilmiştir. Geliştirilen DVM’ ler, normal ve şebeke arızası koşulları sırasında dönüştürücülerin davranışlarını doğru tahmin etmek ve karar vermek için uygun şekilde eğitilmiştir. İnce gauss ve Doğrusal DVM türlerinin performansı, ÇBAG’ ye dayalı bir rüzgâr türbini için eğitim verimliliğinin etkinliği ile karşılaştırılmıştır. DVM' in İnce Gaussian' in doğruluk oranı %100’dür, Doğrusal DVM' in doğruluk oranı ise %22'dir. Simülasyon sonuçları, İnce Gaussian DVM' in, ÇBAG tabanlı bir rüzgâr türbini için Doğrusal DVM' ye kıyasla 3LG şebeke hatasının zararlı etkilerinden daha verimli bir şekilde koruduğunu göstermektedir.

FRT capability enhancement of wind turbine based on DFIG using machine learning

The doubly fed induction generator (DFIG) is very sensitive to the high voltage and current harmful effects that occur during the grid fault. A capacitive bridge type fault current limiter (CBFCL) based on the support vector machine (SVM), which is one of the machine learning (ML) methods, is presented to improve the fault ride-through (FRT) performance of in three phase-to-ground (3LG) symmetric grid fault that may occur in a wind turbine based on DFIG working under normal operating conditions in this study. The machine learning algorithm based on SVM has been implemented in both the control systems of DFIG converters and a control system of CBFCL. Four different SVM classifier algorithms are applied to generate the switching signals of electronic switching elements used in rotor side, grid side converter, and circuit topology of CBFCL. Fine Gaussian, Quadratic, Cubic and Linear kernel functions are preferred in the training of SVM classifiers. The developed SVMs have been suitably trained to true predict and decide behaviours of converters during normal and grid fault conditions. The performance of Fine Gaussian and Linear types of SVM is compared to the effectiveness of training efficiency for a wind turbine based on DFIG. The accuracy rate of the Fine Gaussian of SVM is 100 %, while the accuracy rate of Linear SVM is 22 %. The simulation results show that the Fine Gaussian SVM protects more efficiently from the harmful effects of 3LG grid fault compared to the Linear SVM for a wind turbine based on DFIG.

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