Implementation of a Hybrid ANN-Based Filter for the Reduction of Harmonic Currents

José Abel Obando, V. Serrano
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Abstract

Harmonic distortions caused by non-linear loads (NLLs) affect the behavior of electrical systems, creating harmonics in the fundamental signal. As a result, this deteriorates the power quality. Therefore, this work proposes the implementation of a hybrid filter based on an artificial neural network (ANN) control system, focused on subharmonic, interharmonic and odd harmonic distortions generated by a three-pulse cycloconverter. In addition, a passive double tuned filter was implemented to damp even and odd harmonics. As a result, the simulation performed in MATLAB/SIMULINK showed that the responses produced by the ANN are approximate to the distortions present in the electrical system. Consequently, the levels of total voltage distortions (THDV) and total current distortions (THDI) are reduced. Therefore, the ANN control system improves the quality in the electrical network because the current and voltage harmonics comply with the electrical standards.
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基于混合神经网络的谐波电流抑制滤波器的实现
非线性负载引起的谐波失真会影响电力系统的行为,在基波信号中产生谐波。因此,这会降低电能质量。因此,本工作提出了一种基于人工神经网络(ANN)控制系统的混合滤波器的实现,重点关注由三脉冲环变换器产生的次谐波、间谐波和奇谐波畸变。此外,采用无源双调谐滤波器抑制奇偶谐波。结果,在MATLAB/SIMULINK中进行的仿真表明,人工神经网络产生的响应近似于电气系统中存在的失真。因此,总电压畸变(THDV)和总电流畸变(THDI)的水平降低。因此,人工神经网络控制系统由于电流和电压谐波符合电气标准,提高了电网的质量。
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