论文标题
使用人工神经网络级联的H桥逆变器中THD的最小化
Minimization of THD in Nine Level Cascaded H-Bridge Inverter Using Artificial Neural Network
论文作者
论文摘要
多级逆变器将不同的直流电压转换为交流电压。它对电力行业特别兴趣，尤其是对高力量应用。在电力电子设备中，主要缺点是谐波。可以使用几种控制策略来减少谐波含量和最广泛使用的总谐波失真度（THD）。在此项目中，对开放环和闭环PI控制器和神经网络进行了比较，以预测开关角度以减少谐波。向前神经网络绘制了调制索引和开关角度之间的映射。在预测切换角度之后，执行神经网络拓扑以获得更好的结果。此技术适用于任何类型的多层逆变器，选择级联的H桥多级逆变器。在MATLAB 8.3 SIMULINK中使用正弦PWM技术模拟了九个级别的级联H桥多层逆变器功率电路。比较结果表明，与开放环控制相比，通过神经网络控制，THD降低到约3％。提出和分析结果。
Multilevel inverter converts different level DC voltage to AC voltage. It has wide interest in power industry especially in high power applications. In power electronic equipment the major drawback is the harmonics. Several control strategies are available to reduce the harmonic content and the most widely used measure of Total Harmonic Distortion (THD). In this project, the comparison has been made for the open loop and closed loop PI controller and neural network that predict the switching angle in order to reduce the harmonics. The mapping between Modulation Index and Switching angles are plotted for the forward neural network. After the prediction of switching angles the neural network topologies are executed for better result. This technique is applied for any type of multilevel inverter, Cascaded H-Bridge multilevel inverter is chosen. A nine level Cascaded H-Bridge multilevel inverter power circuit is simulated in MATLAB 8.3 simulink with sinusoidal PWM technique. The comparison results reveal that the THD is reduced to about 3% with neural network control compared to open loop control. The results are presented and analyzed.
