使用MSVM-FSO技术对电网连接系统进行最佳检测和分类
Samuel Raj Daison Stallon1, Ramanpillai Anand2, Ramasamy Kannan2
1Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. daison.electronics@gmail.com.
Environmental science and pollution research international
|April 16, 2024
概括
结合多支持矢量机 (MSVM) 和火虫群优化 (FSO) 的新混合方法有效地诊断混合光伏 (PV) 和风力轮机 (WT) 系统的故障,提高电力质量.
科学领域:
- 可再生能源系统可再生能源系统
- 电气工程 电气工程
- 在电力系统中的人工智能
背景情况:
- 混合光伏 (PV) 和风力轮机 (WT) 系统对于可再生能源发电至关重要.
- 确保这些混合系统的可靠性和功率质量 (PQ) 需要强大的故障诊断和检测 (FDD).
- 现有的FDD方法可能会因为复杂性或在混合系统应用中效率较低而受到影响.
研究的目的:
- 为混合PV-WT系统提出一种新的混合故障诊断和检测 (FDD) 方法.
- 通过准确的故障识别来提高混合系统的功率质量 (PQ).
- 开发低复杂度的FDD解决方案,以提高系统可靠性.
主要方法:
- 开发了一种混合的MSVM-FSO (多支持矢量机器-防火虫群集优化) 方法.
- 用MSVM方法检测网联系统中的故障条件.
- 使用FSO算法来分类电网连接系统中发生的故障类型.
- 分析了系统总线的电压和电流数据,以评估故障事件.
主要成果:
- 拟议的MSVM-FSO方法在故障诊断中实现了99.7%的高精度.
- 该方法的效率达到了98%,超过了现有技术.
- 性能评估是使用MATLAB模拟进行的,与既有方法进行比较.
结论:
- 混合MSVM-FSO方法为混合PV-WT系统的故障诊断和检测提供了一个高度准确和高效的解决方案.
- 这种方法有助于提高可再生能源整合中的电力质量和系统可靠性.
- 拟议的方法为复杂的混合动力电力系统中的FDD提供了一个有希望的,低复杂性的替代方案.
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