对混合动力电源的 entropy-Fused增强的交错几何模式分解质量干扰识别
Chencheng He1, Wenbo Wang1, Xuezhuang E1
1College of Science, Wuhan University of Science and Technology, Wuhan 430065, China.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种用于检测电源质量干扰 (PQD) 的新框架,即使在噪音条件下也能达到高精度. 该方法结合了先进的分解和技术,用于强大的特征提取和分类.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 电力质量干扰 (PQD) 在电网中构成重大运营挑战.
- 对PQD的准确评估在很大程度上依赖于对分类模型的有效特征选择.
- 高维特征向量可以增加模型的复杂性并降低识别速度.
研究的目的:
- 为准确和高效的电力质量干扰识别提出一个强大的特征提取框架.
- 开发一个低维特征向量,以提高分类器的性能.
- 提高电源质量干扰识别模型的准确性和稳定性.
主要方法:
- 改进了对三频段信号分解的综合几何模式分解 (ISGMD).
- 精细的泛化多尺度量子和反向分散的组合用于特征提取.
- 一种用于双层复合分类模型的深度极端学习机器 (ELM) 算法.
主要成果:
- 提出的方法在各种噪音环境中实现了97.3%的平均识别精度.
- 在复杂的混合扰动下,准确度保持在96%以上.
- 与CNN + LSTM相比,在识别准确度上有3.7%的改进,在小数据集上表现优越.
结论:
- 开发的特征提取和分类策略准确地识别了功率质量干扰.
- 与传统方法相比,该方法具有更高的分类准确性和稳定性.
- 在模拟和测量数据上验证的有效性,显示高精度 (99.10%) 和耐噪.
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