基于混合猎粒子小群优化,对多个微电网进行最佳的层次控制
Mohamed Ahmed Ebrahim Mohamed1, Ahmed Mohamed Mahmoud2,3, Ebtisam Mostafa Mohamed Saied2
1Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt. mohamed.mohamed@feng.bu.edu.eg.
Scientific reports
|April 23, 2024
概括
一种新的混合AI优化技术,HYCHOPSO,通过结合猎优化和粒子优化来改善多个微网 (MMG) 的控制. 这提高了智能电网的能源转型,可靠性和效率.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 微电网正在越来越多地将可再生能源 (RES) 和储能系统 (ESS) 整合到配电网络 (DN) 中.
- 在能源转型期间协调多个微电网 (MMG) 对传统方法提出了重大控制挑战.
- 人工智能 (AI) 为增强智能电网中的MMG动态运行和控制提供了一个有希望的解决方案.
研究的目的:
- 引入创新的混合优化算法HYCHOPSO,以改善MMG控制.
- 评估HYCHOPSO的性能与微电网应用中的现有优化技术相比.
- 通过先进的控制策略,提高微电网运营的效率,可靠性和可扩展性.
主要方法:
- 开发一种混合优化技术HYCHOPSO,它结合了猎优化 (CHO) 和粒子优化 (PSO).
- 进行了广泛的基准测试,以验证HYCHOPSO的趋同表现和优于个别CHO和PSO的优势.
- 对HYCHOPSO与各种元启发式优化方法进行比较分析,以优化微电网等级控制系统中的比例整合 (PI) 控制器参数.
主要成果:
- HYCHOPSO展示了卓越的融合性能,在不到50次代中获得最佳分数,与其他算法稳定约200次代相比.
- 在整个基准函数中,HYCHOPSO始终达到较低的平均值,并且得分接近最佳值,这表明了强大的趋同.
- 在HYCHOPSO优化的PI控制器有效地减少错误,提高系统可靠性,功率共享精度,电压/频率稳定性,并在动态MMG操作期间无过渡.
结论:
- 在优化MMG的控制参数方面,HYCHOPSO算法提供了显著的进步.
- 这种混合方法提高了微电网可靠性,灵活性,可扩展性和实体应用中的稳定性.
- HYCHOPSO为微电网能源管理和控制的复杂挑战提供了实用和高效的解决方案.
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