优化与电网连接的光伏系统,使用新的超扭转滑动模式控制器来实现实时电源管理
Bhabasis Mohapatra1, Binod Kumar Sahu1, Swagat Pati2
1Department of Electrical Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India.
Scientific reports
|February 27, 2024
概括
本研究介绍了改进的人工优化算法 (IAOA),用于加强与电网连接的光伏系统的控制. IAOA 改进了主动和反应功率控制,优于现有的清洁能源整合方法.
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
- 控制理论 控制理论
背景情况:
- 太阳能光伏 (PV) 系统日益融入电网,需要先进的控制策略.
- 联网光伏 (GCPV) 系统需要精确的主动和反应功率控制,以保持稳定性和效率.
- 现有的优化算法,如 CAOA,在融合和探索方面存在局限性.
研究的目的:
- 通过合并粒子群优化 (PSO) 和CAOA,提出一个创新的优化算法,即改进的人工优化算法 (IAOA).
- 实施基于IAOA的滑动模式控制器 (SMC) 以在GCPV系统中有效管理主动和反应功率.
- 用基准函数和实时模拟来评估IAOA的性能和适用性.
主要方法:
- 通过结合PSO和CAOA原则,发展IAOA.
- 基于IAOA的滑动模式控制器 (ST-SMC) 的实施,用于功率控制.
- 使用四个基准函数和在MATLAB中的模拟进行性能评估.
- 在40千瓦的GCPV系统上使用OPAL-RT 4510实时模拟器进行验证.
主要成果:
- 在基准功能测试中,IAOA表现出卓越的表现.
- 基于IAOA的ST-SMC实现了对主动和反应功率控制的显著缩短的沉降时间 (最低分别为0.01012s和0.5075s).
- 拟议的控制器在模拟中超过了基于PSO和基于CAOA的ST-SMC.
结论:
- 对于高级控制应用来说,IAOA是一个有前途的优化技术.
- 基于IAOA的ST-SMC为GCPV系统中的主动和反应功率控制提供了有效和创新的解决方案.
- 实时模拟验证了可再生能源整合的拟议控制策略的实际适用性和效率.
关键词:
传统的算术优化算法 (CAOA)连接到电网的光伏系统 (GCPV)改进的算术优化算法 (IAOA)粒子群集优化 (PSO) 是一种太阳能 (PV) 的光伏 (PV) 的比例整合 (PI) 控制器控制器超扭转的滑动模式控制器 (ST-SMC)更多相关视频
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