在可再生能源系统中使用混合深度学习来识别电源质量干扰
1Department of Electronics and Communication Engineering, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India. m.peruman@gmail.com.
Scientific reports
|November 29, 2025
概括
一种新的混合深度学习方法可以准确地诊断混合风-太阳能光伏 (Wind-SPV) 网络中的电源质量干扰 (PQD). 这种方法提高了电力质量,支持向可再生能源的过渡.
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
- 在电力系统中的人工智能
背景情况:
- 越来越多的混合风能太阳能光伏 (Wind-SPV) 系统的整合需要强大的电力质量监测.
- 确保高功率质量对于可再生能源网络的可靠性和客户满意度至关重要.
- 检测电源质量干扰 (PQD) 的传统方法通常在分辨率或计算效率方面存在局限性.
研究的目的:
- 引入一种基于深度学习的新型混合式方法,用于风能-SPV集成网络中PQD的特定诊断.
- 评估拟议方法在混合可再生能源框架内提高电力质量的有效性.
- 为了证明拟议技术在传统的PQD传感方法上的优越性.
主要方法:
- 一个混合深度学习模型,将连续波形变换 (CWT) 刻度图与深度神经网络 (ResNet,VGG-Net) 结合起来.
- 邻近组件分析 (NCA) 和支持矢量机 (SVM) 的集成用于分类.
- 使用MATLAB/Simulink在定制的IEEE9总线和IEEE13总线测试系统上进行的模拟.
主要成果:
- 拟议的方法实现了高准确性 (IEEE 9 总线上98.54%,IEEE 13 总线上97.17%),精度和回忆.
- 性能明显优于常规技术,如富里埃变形和离散波纹变形.
- 在复杂的,现实世界的混合可再生能源场景中证明了有效性.
结论:
- 开发的混合深度学习方法为Wind-SPV网络中的PQD诊断提供了有效的解决方案.
- 这种进步有助于提高电力质量,并支持全球向可再生能源的转变.
- 该方法为现有的PQD检测技术提供了可靠且计算效率高的替代方案.
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