通过基于ANN的直接扭矩控制,使用实时模拟来提高光伏水系统的性能.
Ikram Saady1,2, Btissam Majout1, Ismail El Kafazi2
1Laboratory of Engineering Modelling and Systems Analysis, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Scientific reports
|February 2, 2025
概括
人工神经网络通过改进最大功率点跟踪 (MPPT) 和直接扭矩控制 (DTC) 来增强光伏水系统 (PVWPS). 这导致波纹显著减少,可再生水的响应时间更快.
科学领域:
- 可再生能源系统可再生能源系统
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
背景情况:
- 光伏水系统 (PVWPS) 对农村可再生能源至关重要,提供独立性和成本节约.
- 传统的PVWPS控制器,包括MPPT和直接扭矩控制 (DTC),面临诸如电力提取效率低下和电机控制缺陷等挑战.
- 现有的DTC方法存在流量/扭矩波动和低速性能差.
研究的目的:
- 引入人工神经网络 (ANN) 来增强PVWPS控制器.
- 通过先进的控制策略来提高PVWPS的效率和性能.
- 为解决光伏水应用常规MPPT和DTC的局限性.
主要方法:
- 实现基于ANN的最大功率点跟踪 (MPPT) 控制器,以实现最佳的光伏阵列电源提取.
- 基于ANN的直接扭矩控制 (DTC) 策略的开发,取代传统的组件,如歇斯底里比较器和切换表.
- 使用MATLAB/Simulink对PVWPS进行建模和模拟,并通过dSPACE DS1104板实时模拟进行验证.
主要成果:
- 在感应电机中显著减少了流量波纹 (75.51%) 和扭矩波纹 (77.5%).
- 系统响应时间提高了44.79%,水输出量增加.
- 实时模拟结果验证了拟议的基于ANN的控制策略的有效性.
结论:
- 基于ANN的控制器为PVWPS提供了优越的解决方案,克服了传统方法的局限性.
- 拟议的系统证明了光伏水的提高效率,稳定性和性能.
- 这一进步有助于为供水提供更可靠,更有效的可再生能源解决方案.
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