在功率变压器中使用机器学习方法定位部分放电
Farzin Khodaveisi1, Hamidreza Karami2, Matin Zarei Karimpour3
1Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran.
Scientific reports
|May 23, 2024
概括
本研究比较了机器学习 (ML) 和深度学习 (DL) 方法来定位功率变压器中的部分放电 (PD). 卷积神经网络 (CNN) 在使用电场测量预测PD位置方面表现出最高的准确性.
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:
- 功率变压器中的部分放电 (PD) 对电网稳定性构成重大风险.
- 准确地定位PD对于有效的维护和故障预防至关重要.
- 现有的本地化方法经常面临复杂的变压器几何和信号噪声的挑战.
研究的目的:
- 为了比较各种机器学习 (ML) 和深度学习 (DL) 方法在功率变压器中3D PD定位的有效性.
- 根据输入信号,方法和准确度指标评估不同ML / DL模型的性能.
- 确定最合适的ML/DL方法,以准确识别PD源.
主要方法:
- 研究了用于PD定位的支持向量机器 (SVM) 和卷积神经网络 (CNN).
- 使用单传感器电场测量作为输入数据.
- 基于输入信号特征,核心算法,相关系数和根平均平方误差 (RMSE) 的分析方法.
- 进行了多个案例研究,使用不同的传感器位置,信号频率和变压器大小.
主要成果:
- 卷积神经网络 (CNN) 在PD本地化准确性方面显著超过了其他ML/DL方法.
- 该研究使用相关系数和RMSE量化了性能差异.
- 模型性能因特定的案例研究属性而异,例如传感器的位置和信号频率.
结论:
- 在电力变压器中,CNN模型为精确的3D部分放电定位提供了卓越的解决方案.
- 这些发现为开发电力设备的先进诊断工具提供了宝贵的见解.
- 进一步的研究可以探索混合模型和实时实施,以提高电网可靠性.
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