TLBO trained an ANN-based DG integrated Shunt Active Power Filter to Improve Power Quality

Venkata Anjani Kumar Gaddam, Manubolu Damodar Reddy
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Abstract

In terms of power quality, the rising number of nonlinear loads in modern use has caused warning signs for power system and power engineering professionals. Every day, utilities have to deal with harmonic distortion caused by a growing number of non-linear power electronic equipment. To keep the system's power supply in good condition, a shunt active filter is used to filter out unwanted harmonics in the signal. This study presents a practical and low-cost method for reducing harmonics and enhancing distribution network power quality by means of the use of PV-integrated Shunt Active Power Filters (SAPF). With a teaching-learning-based optimized artificial neural network controller (TLBO-ANN) and the required DC power is extracted from the PV module. SAPF's TLBO-ANN algorithms are intended to increase system performance by reducing total harmonic distortion (THD). Here, the research work was performed in three stages to mitigate grid current harmonics. The first-stage SAPF system comprises a three-prong voltage source converter and uses DC power derived from photovoltaic panels. The P&O algorithm is used to get the maximum power out of a photovoltaic array. In the second stage, the BBO algorithm is used to fine-tune a conventional PI controller, resulting in values for and that increase the controller's performance. Furthermore, it is intended to use the BBO-PI controller's input and output values as training data for the ANN controller. This ANN controller is currently being tuned with the TLBO algorithm to find optimal values for the weight and bias parameters. In the third stage, the converter in PV-SAPF will inject the active power required by the load by using active current control theory, which means the inverter of SAPF is working like DG as well as the active power filter. Employing MATLAB simulations, we concluded that the proposed method is extremely adaptable and highly efficient in lowering harmonic currents that are brought on by non-linear loads.
TLBO 训练了一种基于 ANN 的 DG 集成并联有源电力滤波器,以改善电能质量
在电能质量方面,现代使用的非线性负载数量不断增加,给电力系统和电力工程专业人员带来了警示信号。每天,电力公司都要处理越来越多的非线性电力电子设备造成的谐波失真。为了保持系统电源的良好状态,需要使用并联有源滤波器来滤除信号中不需要的谐波。本研究提出了一种实用、低成本的方法,通过使用光伏并联有源电力滤波器(SAPF)来减少谐波并提高配电网电能质量。通过基于教学的优化人工神经网络控制器(TLBO-ANN),从光伏组件中提取所需的直流电。SAPF 的 TLBO-ANN 算法旨在通过降低总谐波失真(THD)来提高系统性能。在此,研究工作分三个阶段进行,以减少电网电流谐波。第一阶段 SAPF 系统包括一个三相电压源转换器,使用光伏电池板产生的直流电。P&O 算法用于获取光伏阵列的最大功率。在第二阶段,使用 BBO 算法对传统的 PI 控制器进行微调,从而得出能提高控制器性能的和值。此外,还打算使用 BBO-PI 控制器的输入和输出值作为 ANN 控制器的训练数据。目前正在使用 TLBO 算法对 ANN 控制器进行调整,以找到权重和偏置参数的最佳值。在第三阶段,PV-SAPF 中的变流器将利用有功电流控制理论注入负载所需的有功功率,这意味着 SAPF 的变流器既像 DG 一样工作,也像有功功率滤波器一样工作。通过 MATLAB 仿真,我们得出结论,所提出的方法适应性极强,能高效降低非线性负载带来的谐波电流。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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