期刊论文

中文

电力科学与工程

1672-0792

2015

31

12

9-15

10.3969/j.issn.1672-0792.2015.12.002

14k001:湖南省高等学校创新平台开放基金 2015GK3018:湖南省科技厅科技项目 k1403041-11:长沙市科技计划

本校为第一机构

为了改善感应电机传统直接转矩控制（DTC）的性能缺陷,特别是低速状态下定子磁链畸变、定子电流谐波大、电磁转矩脉动大的缺点,提出一种基于小波神经网络（WNN）的新型非线性自回归移动平均模型（NARMA）。该模型根据H.Akaike的最终预测误差（FPE）准则确定WNN模型中所需的最佳小波个数。小波神经网络有很强的自学习能力,经过训练可很好地识别DTC系统,基于WNN的NARMA速度控制器可代替PI控制器控制感应电机的转矩。理论分析和仿真表明,该方法是非常有效的。

In order to amend the performance deficiencies of the traditional direct torque control ( DTC) , especial-ly at low speeds , where there are some shortcomings such as stator flux distortion , large stator current harmonics and electromagnetic torque ripple , a novel model denoted as nonlinear autoregressive moving average ( NARMA) model based on wavelet neural networks ( WNN) is presented.According to the Akaike's final prediction error (FPE) criterion, the optimum number of wavelets to be used in the WNN model is selected .The WNN has so strong learning ability as to be trained well to identi...