人工神经网络控制的DSTATCOM用于缓解太阳能光伏和风能系统的电力质量问题
Mohammed Mujahid Irfan1, Mohammed Alharbi2, C H Hussaian Basha3
1Department of Electrical and Electronics Engineering, SR University, Warangal, Telangana, 506371, India. irfan.mujahid066@gmail.com.
Scientific reports
|February 11, 2025
概括
本研究介绍了基于人工神经网络 (ANN) 的分布静态同步补偿器 (DSTATCOM),以提高光伏 (PV) 和风力发电系统的电力质量. 新的XANN方法有效地减轻了波,在具有挑战性的非线性负载条件下确保了稳定的性能.
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
- 电力质量 电力质量 电力质量
背景情况:
- 全球日益增长的能源需求需要可持续的替代方案来减少化石燃料的使用.
- 光伏 (PV) 和风力发电是主要的可再生能源,但它们的整合给电力质量带来了挑战.
- 太阳能和风能系统中功率电子产品的波扭曲威胁到电网稳定性.
研究的目的:
- 提出和评估基于人工神经网络 (ANN) 的分布静态同步补偿器 (DSTATCOM),以缓解电源质量问题.
- 为了解决在可变负载条件下传统的DSTATCOM控制方法的局限性.
- 提高集成光伏和风能系统的可靠性和效率.
主要方法:
- 开发一个使用增强的人工神经网络 (XANN) 方法的DSTATCOM控制模型.
- 在MATLAB中对拟议模型进行模拟,以评估其性能.
- 通过实时实验设置验证模拟结果.
主要成果:
- 基于XANN的DSTATCOM有效地减轻了光伏风力发电系统中的波扭曲.
- 拟议的模型表现出卓越的性能,特别是在不均和非线性负载场景下.
- 观察到总体功率质量的显著改善.
结论:
- 基于ANN的DSTATCOM,特别是XANN方法,为可再生能源系统的电力质量提升提供了强大的解决方案.
- 这项技术对于最大限度地发挥可持续能源整合的潜力至关重要.
- 经过验证的结果证实了拟议方法在现实应用中的有效性.
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