中文摘要：

有源电力滤波器检测谐波电流的实时性、精确性对电能质量的提高至关重要,文中提出了基于非线性最小二乘法与自适应人工神经网络结合的检测方法。非线性最小二乘法用于检测基波电压的频率,自适应人工神经网络用于检测基波电压的初始相位和基波电流的幅值,由基波电压的频率和初始相位获得单位幅值的基波电流。文中方法在0.02 s内可准确检测出基波和谐波电流,检测精度较传统方法有显著提高,通过仿真验证了该方法的有效性和优越性。

英文摘要：

The accuracy and real-time performance of APF detecting the harmonic current is vital to improving the quality of electric energy. The method based on NLS （non-linear least squares） and ADALINE is proposed in this paper. NLS is used to estimate the frequency of the network voltage. ANN is used to estimate the initial phase of the network voltage and the amplitude of the network fundamental active current. Through the frequency and the initial phase of the network voltage, the sine signal whose frequency and initial phase is the same as the network voltage is genera- ted. Compared with the traditional method, the time of detecting the phase, frequency, and amplitude of the network fundamental active current is apparently shortened and the accuracy is obviously improved. The method proposed precisely detects the fundamental current in 0. 02 s. Simulation shows the effectiveness and superiority of this method.