使用多级全球卷积神经网络和SDTransformer方法对微电网中的电力质量干扰进行分类
Junzhuo Jiang1,2, Hao Wu1,2, Changhua Zhong1,2
1Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin, Sichuan, China.
PloS one
|February 12, 2025
概括
这项研究引入了一种新的MGCNN-SD变压器模型,以准确检测微电网中的电力质量干扰 (PQD). 先进的AI模型提高了可再生能源系统的稳定性和效率.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电力系统 电力系统
背景情况:
- 越来越多地采用可再生能源 (光伏,风能) 和电力电子设备,导致更多的电力质量干扰事件 (PQD).
- PQD,如波和电压下降,会对微电网的稳定性和电力设备的效率产生负面影响.
- 准确识别PQD对于保持可靠的电力系统至关重要.
研究的目的:
- 开发和评估一种新型模型,以提高微电网中电力质量干扰识别的准确性.
- 为应对由于可再生能源整合和先进的电力电子产品而增加的PQD所带来的挑战.
- 提高电力质量监测系统的稳定性和一般化能力.
主要方法:
- 引入一个多层全球卷积神经网络与简化双层变压器模型 (MGCNN-SDTransformer) 结合.
- 使用多级卷积和1D-全球注意力机制 (1D-GAM) 来处理1D时间序列功率质量信号以提取特征.
- 在SDTransformer中使用多头自我注意 (MSA) 和多层感知器 (MLP) 进行更深入的特征探索.
- 使用完全连接层和Softmax分类器对干扰进行分类.
主要成果:
- MGCNN-SD转换器模型有效地保留了原始信号属性,同时提取了复杂的特征.
- 拟议的模型表现出强大的抗噪力和增强的泛化技能.
- 显著提高了微电网中的电力质量问题的检测准确度.
结论:
- MGCNN-SD变压器模型在准确识别微电网中的电力质量干扰方面取得了重大进展.
- 这种方法提高了集成可再生能源的微电网的稳定性和效率.
- 该模型的稳定性和准确性使其成为用于电力系统监控和管理的宝贵工具.
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