在开关器件中部分放电故障的模式识别使用逆向传播神经网络,优化通过改进的虫搜索算法
Zhangjun Fei1, Yiying Li1, Shiyou Yang1
1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究介绍了一种改进的Mantis搜索算法 (MSA),以优化反向传播神经网络 (BPNN),以在开关设备中精确的部分放电模式识别 (PDPR),提高电力系统的稳定性.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能的人工智能
背景情况:
- 部分放电 (PD) 是一种危害开关装置绝缘的关键现象,可能导致设备故障.
- 准确检测PD类型 (金属颗粒,悬浮,爬行放电) 对于评估绝缘的严重性和防止事故至关重要.
- 部分放电模式识别 (PDPR) 对于早期识别电源系统的绝缘缺陷至关重要.
研究的目的:
- 提出一个优化的反向传播神经网络 (BPNN),用于在开关设备中增强部分放电模式识别 (PDPR).
- 通过解决对初始参数的敏感性来提高BPNN的稳定性和概括能力.
- 通过缩小维度来降低计算复杂性并提高识别效率.
主要方法:
- 开发了一种改进的Mantis搜索算法 (MSA),以优化BPNN参数,结合边界处理和自适应策略.
- 使用主要组件分析 (PCA) 来减少PD特征数据的维度,从14个特征减少到7个特征.
- 优化的BPNN与决策树 (DT),k-近邻 (KNN) 和支持向量机 (SVM) 分类器进行了评估.
主要成果:
- 使用PCA的维度缩小使BPNN参数数量从183减少到113,大大节省了计算时间.
- 拟议的MSA优化的BPNN实现了金属颗粒放电和悬浮放电类型的最高识别精度.
- 尽管特征尺寸大幅减少,但仍保持了可比的识别精度.
结论:
- 开发的MSA优化的BPNN为交换机中的PDPR提供了强大而高效的解决方案.
- 这种方法提高了对绝缘缺陷的早期检测,有助于提高电力系统的可靠性和安全性.
- 集成PCA和MSA提供了一个计算效率高,准确的方法来监测开关设备的状态.
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