Sevugan Rajesh J, Karthikeyan R, Revathi R
ECE Department, Dr. NGP Institute of Technology, Coimbatore, India.
EEE Department, M. Kumarasamy College of Engineering, Karur, India.
Sci Rep. 2024 Oct 29;14(1):25963. doi: 10.1038/s41598-024-77822-8.
In this study, a novel water pumping module fed by grid interactive Photo-Voltaic with a bidirectional Power Flow Control was proposed. In addition to improving the pumping system's reliability, a water pump is powered by a brushless DC motor drive. This method enables the pump to work at its maximum capacity for the entirety of that day, regardless of the weather. The entire system becomes more reliable as a result of the motor's increased use of photovoltaic (PV) generated power for pumping applications. Maximum Power Point Tracking (MPPT) controller incorporating Machine Learning algorithm drives bridgeless greater static gain DCDC converter to achieve higher power generation point and increment PV efficiency. The PV array's operation would be managed using the ML back propagation technology to capture the most electricity under any ecological circumstance. A BLDC motor is fed by a Voltage Source Inverter (VSI) that includes a DC bus controlled in both directions by a unit vector template (UVT) approach incorporated in a single-phase voltage source converter (VSC). Additionally, utilizing a PI controller to manage the DC capacitor voltage in the UVT controller at a particular level is not appropriate for the increased PQ capabilities. However, due to tuning problems with the current controller, this controller is unpopular. The aforementioned problems are resolved by employing a unique intelligent-based fuzzy logic controller that achieves good performance features. In this technique, the function of a PV array at its Maximum Power Point (MPP), as well as power quality enhancements and a decrease in Total Harmonic Distortion (THD) of the grid are accomplished. The proposed PI controller attains a significant voltage THD of 3.736. The PI controller, on the other hand, managed to achieve a load voltage THD of 2.629%. The ANFIS method, whose value is 1.739%, is discovered to have a lower THD than all remotes with improved features, it lessens abrupt swings while maintaining steady DC-link voltage.
在本研究中，提出了一种由具有双向功率流控制的并网交互式光伏发电供电的新型水泵模块。除了提高抽水系统的可靠性外，水泵由无刷直流电机驱动。这种方法使水泵能够在一整天内以其最大容量工作，而不受天气影响。由于电机更多地使用光伏发电用于抽水应用，整个系统变得更加可靠。采用机器学习算法的最大功率点跟踪（MPPT）控制器驱动无桥更高静态增益DCDC转换器，以实现更高的发电点并提高光伏效率。将使用ML反向传播技术管理光伏阵列的运行，以在任何生态环境下捕获最多的电力。无刷直流电机由电压源逆变器（VSI）供电，该逆变器包括一个直流母线由单相电压源转换器（VSC）中采用的单位矢量模板（UVT）方法双向控制。此外，在UVT控制器中使用PI控制器将直流电容器电压控制在特定水平不适用于增加的PQ能力。然而，由于电流控制器的调谐问题，该控制器不受欢迎。通过采用独特的基于智能的模糊逻辑控制器解决了上述问题，该控制器具有良好性能特征。在该技术中，实现了光伏阵列在其最大功率点（MPP）的功能，以及电能质量的提高和电网总谐波失真（THD）的降低。所提出的PI控制器实现了3.736的显著电压THD。另一方面，PI控制器设法实现了2.629%的负载电压THD。发现值为1.739%的ANFIS方法具有比所有具有改进功能的遥控器更低的THD，它减少了突然波动，同时保持直流链路电压稳定。
