基于可再生能源的独立混合系统的智能控制和管理
Abdelhak Kechida1, Djamal Gozim2, Belgacem Toual3
1Applied Automation and Industrial Diagnostics Laboratory, Ziane Achour University Djelfa, Djelfa, Algeria. abdelhak.kechida@univ-djelfa.dz.
Scientific reports
|December 31, 2024
概括
本研究介绍了一个智能混合可再生能源系统,用于高效的电力管理. 拟议的自适应神经模糊推理系统 (ANFIS) 最大限度地提高了能源的产生,并确保了稳定的电力供应,非常适合远程地点.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 控制系统 控制系统
背景情况:
- 独立的混合系统需要高效的能源管理以实现最佳性能.
- 整合光伏系统 (PVS),风能转换系统 (WECS) 和电池存储系统 (BSS) 提出了控制挑战.
- 智能管理对于最大化能源利用和确保电力稳定至关重要.
研究的目的:
- 开发和评估一个独立的混合可再生能源系统的智能控制战略.
- 通过先进的MPPT技术提高光伏和风能转换的效率.
- 确保稳定的直流总线电压,并有效地管理各种交流和直流负载.
主要方法:
- 基于自适应神经模糊推理系统 (ANFIS) 的最大功率点跟踪 (MPPT) 技术的实施,用于PVS和WECS.
- 使用ANFIS-Proportional-Integral (ANFIS-PI) 控制器进行双向转换器控制和直流总线电压稳定.
- 开发基于模糊逻辑的算法,用于智能负载管理,考虑电池状态,太阳辐射和风速.
主要成果:
- 基于ANFIS的MPPT技术在Perturb and Observe (P&O) 和模糊逻辑控制器 (FLC) 方法相比显示出更高的性能.
- ANFIS-PI控制器成功稳定了直流总线电压.
- 模糊逻辑负载管理算法确保充分利用产生的可再生能源.
- 在MATLAB/Simulink中的模拟证实了拟议的控制策略的有效性.
结论:
- 拟议的智能管理和控制系统显著提高了混合可再生能源系统的效率和可靠性.
- 基于ANFIS的方法提供了一个强大的解决方案,可以最大限度地从PVS和WECS获得能源.
- 开发的系统显示出在需要自主和稳定的电力供应的偏远地区有很大的应用潜力.
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