基于卷积神经网络的电力线故障诊断
1Tangshan Power Supply Company State Grid Jibei Electric Power Co.Ltd, Tangshan 063000, Hebei, China.
Heliyon
|April 17, 2024
概括
与其他深度学习模型相比,卷积神经网络 (CNN) 为诊断电线故障提供了更高的准确性和稳定性. 优化CNN结构,比如减少批量数量和增加训练次数,可以提高故障确定精度.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 电力安全对于国家和经济稳定至关重要.
- 电线故障严重扰乱社会生产和日常生活.
- 准确和及时的故障诊断对于保持电网可靠性至关重要.
研究的目的:
- 调查卷积神经网络 (CNN) 在电力线故障诊断方面的有效性.
- 将CNN与其他深度学习模型 (如递归神经网络和深度信念网络) 的性能进行比较.
- 分析CNN结构参数对故障诊断准确性和稳定性的影响.
主要方法:
- 对卷积神经网络 (CNN) 的结构和工作原理的分析.
- 应用CNN模型用于诊断电力线路故障.
- 结果的模拟和分析,比较CNN与递归神经网络和深信网络.
主要成果:
- CNN实现了最高的稳定故障诊断准确度 (100%),超过了递归神经网络 (93.4%) 和深信网络 (91.5%).
- CNNs表现出卓越的稳定性,准确度标准偏差接近0.
- 通过减少批量数量和增加培训次数来优化CNN,可以积极影响故障诊断的准确性.
结论:
- 在电力线故障诊断方面,CNN是非常有效和稳定的.
- CNNs的架构和训练参数显著影响其诊断性能.
- CNNs代表了一种有希望的深度学习方法,用于提高电力系统的可靠性.
相关概念视频
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When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
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Three-Phase Short Circuit—Unloaded Synchronous Machine
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Bus Impedance Matrix
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Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
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Fast Decoupled and DC Powerflow
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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