توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Real–time Selective Harmonic Minimization in unequal DC Source Inverters with PSO and RBFNN
نویسنده(ها): ، ،
اطلاعات انتشار: بیست و ششمین کنفرانس بین المللی برق، سال
تعداد صفحات: ۶
In this paper a RBF neural network base harmonic elimination in multilevel inverters supplied from unequal dc sources is presented. This method uses PSO algorithm to obtain switching angles offline for different DC source values and then BRF neural network is trained to determine the optimum switching angles online. The variation of the dc sources affects the values of the switching angles required for each specific harmonic profile, as well as increases the difficulty of the harmonic elimination’s equations. Simulation results show the good performance of presented approach. As opposed to previous research in this area, the DC sources feeding the multilevel inverter are considered to be varying in time and selective harmonic eliminating is done. This implies that each one of the DC sources of this topology can have different values at any time but the output fundamental voltage will stay constant and the harmonic will still meet the specifications.<\div>

۲Speed Observer Based on ICA Trained Neural Network in DTC drive of IPMSM
نویسنده(ها): ،
اطلاعات انتشار: بیست و ششمین کنفرانس بین المللی برق، سال
تعداد صفحات: ۶
In this paper a speed observer based on Imperialist Competitive Algorithm (ICA) trained artificial neural network is presented. The proposed speed observer is used in sensorless Direct Torque Control (DTC) IPMSM drive scheme. A multilayer perception is trained using imperialist competitive algorithm to estimate the rotor speed. Due to artificial neural network characteristics the proposed speed observer works in wide range speed as opposed to previous observers that doesn’t works low speed or high speeds. Since neural network is trained with ICA, optimum weights of neural network are obtained. Simulation results on different conditions show the good performance of proposed speed observer.<\div>

۳RBF and MLP Neural Network Speed Observer for Sensorless DTC Drive of IPMSM
اطلاعات انتشار: بیستمین کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۵
In this paper neural network speed observers for sensorless DTC drive of IPMSM are presented and comparisons between MLP and RBF neural networks inthis case, have done. Introduced neural network based speed observers are trained by Imperialist Competitive Algorithm (ICA). Due to artificial neural networkcharacteristics the proposed speed observers work in wide range speed as opposed to previous observers that doesn’t works in low speed or high speeds. Since neural network is trained with ICA, optimum weights of neural network are obtained. Simulation results on different conditions showthe good performance of proposed speed observers. However simulation shows that, RBFNN base speed observer has better performance than MLP neural networkobserver, both observer have good performance in wide range speed. In the other word operation in both low andhigh speeds is the main advantage of presented speed observers.<\div>
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