Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump

Divya Shetty, J. N. Sabhahit
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Abstract

The use of Solar Powered Water Pumps (SPWP) has emerged as a significant advancement in irrigation systems, offering a viable alternative to electricity and diesel-based pumping methods. The appeal of SPWPs to farmers lies in their low maintenance costs and the incentives provided by government agencies to support sustainable and cost-effective agricultural practices. However, a critical challenge faced by solar photovoltaic (PV) systems is their susceptibility to power loss under partial shading conditions, which can persist for extended periods, ultimately reducing system efficiency. To address this issue, this paper proposes the integration of Maximum Power Point Tracking (MPPT) controllers with efficient algorithms designed to identify the peak power during shading events. In this study, a hybrid approach combining Grey Wolf Optimization (GWO) and Incremental Conductance (INC) is employed to maximize the power output of SPWPs driven by an induction motor under partial shading conditions. In order to achieve faster convergence to the global peak, GWO handles the first stages of MPPT and then INC algorithm is employed at the end of the MPPT process.  This method reduces the computations of GWO and streamlines the search space. The paper evaluates the performance of the induction motor in terms of speed settling time and torque ripple. To validate the effectiveness of the GWO-INC hybrid approach, simulations are conducted using the MATLAB Simulink platform. The outcomes are then compared with results obtained from various well-known approaches, including Particle Swarm Optimization – Perturb and Observe (PSO-PO), PSO-INC, and GWO-PO, illustrating the superiority of the GWO-INC hybrid approach in enhancing the efficiency and performance of solar water pumps during shading. The GWO-INC excels with 99.6% accuracy in uniform shading and 99.8% in partial shading. It achieves convergence in a mere 0.55 seconds under uniform shading conditions and only 0.42 seconds when partial shading is present. Moreover, it significantly reduces torque oscillations, with a torque ripple of  8.26% in cases of uniform shading and 10.56% in partial shading.
基于灰狼优化和增量电导的混合 MPPT 技术用于太阳能感应电机驱动的水泵
太阳能水泵(SPWP)的使用已成为灌溉系统的一大进步,为电力和柴油机抽水方法提供了可行的替代方案。太阳能水泵对农民的吸引力在于其低廉的维护成本,以及政府机构为支持可持续和具有成本效益的农业实践而提供的激励措施。然而,太阳能光伏(PV)系统面临的一个关键挑战是,在部分遮阳条件下容易出现功率损耗,这种情况可能会持续较长时间,最终降低系统效率。为解决这一问题,本文提出将最大功率点跟踪 (MPPT) 控制器与旨在识别遮阳事件期间峰值功率的高效算法相结合。在这项研究中,采用了灰狼优化 (GWO) 和增量电导 (INC) 相结合的混合方法,以最大限度地提高部分遮阳条件下由感应电机驱动的 SPWP 的功率输出。为了更快地收敛到全局峰值,GWO 处理 MPPT 的第一阶段,然后在 MPPT 过程的最后阶段采用 INC 算法。 这种方法减少了 GWO 的计算量,简化了搜索空间。本文从速度稳定时间和转矩纹波两个方面评估了感应电机的性能。为验证 GWO-INC 混合方法的有效性,使用 MATLAB Simulink 平台进行了仿真。仿真结果与粒子群优化-扰动和观测(PSO-PO)、PSO-INC 和 GWO-PO 等各种著名方法的结果进行了比较,说明 GWO-INC 混合方法在提高遮阳期间太阳能水泵的效率和性能方面具有优越性。GWO-INC 在均匀遮阳和部分遮阳情况下的准确率分别达到 99.6% 和 99.8%。在均匀遮光条件下,它仅用 0.55 秒就实现了收敛,而在部分遮光条件下,仅用 0.42 秒就实现了收敛。此外,它还大大减少了扭矩振荡,在均匀遮光情况下,扭矩纹波为 8.26%,在部分遮光情况下为 10.56%。
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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