基于玻璃盒的新型可解释增强机器用于电力传输系统中检测故障
Iqra Akhtar1, Shahid Atiq1, Muhammad Umair Shahid1
1Department of Electrical and Biomedical Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
PloS one
|August 28, 2024
概括
本研究介绍了一种优化的可解释增强 (EB) 模型,用于电力传输系统中先进的故障检测. 新的EB方法在识别和分类故障方面达到99%的准确性,提高了电网可靠性和智能电网技术.
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:
- 可靠的电力传输对于不间断的供应至关重要.
- 传输系统的故障会导致中断,经济损失和安全隐患.
- 有效的故障检测和分类对于电网保护至关重要.
研究的目的:
- 开发和评估先进的人工神经网络方法论,用于电力传输系统的故障检测和分类.
- 要区分单相,三相和三相对称断层.
- 为增强故障检测提出一个优化,可解释的可解释增强 (EB) 模型.
主要方法:
- 使用MATLAB来建模和模拟使用线路电流和电压的故障场景.
- 应用于信号数据的时间序列分析和基于SMOTE的过量抽样,用于数据集平衡.
- 开发并比较了四个机器学习和一个深度学习模型,专注于优化可解释增强 (EB) 方法.
- 集成的超参数优化,k倍验证和可解释的人工智能 (XAI) 用于模型评估.
主要成果:
- 拟议的可解释增速 (EB) 模型在检测和分类输电线路故障方面实现了99%的效率.
- 与传统故障检测方法相比,其表现优越.
- 根据XAI的分析,可以了解EB模型的决策过程.
结论:
- 优化的可解释增强 (EB) 模型为电力传输中故障检测提供了高效和可解释的解决方案.
- 这项研究为推进智能电网技术提供了可扩展和适应的方法.
- 这些发现为更安全,更高效的电力传输系统铺平了道路.
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