| 引用本文: | 韩素敏,张树志,吕文龙,等.对称点模式与ECA-ConvNeXt结合的并网逆变器故障诊断[J].电力系统保护与控制,2026,54(02):139-150. |
| HAN Sumin,ZHANG Shuzhi,LÜ Wenlong,et al.Fault diagnosis of grid-connected inverters based on symmetric-point pattern and ECA-ConvNeXt[J].Power System Protection and Control,2026,54(02):139-150 |
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| 摘要: |
| 为解决并网中性点钳位型逆变器同桥臂双管和内侧单管开路故障诊断困难，以及过多检测信号引起的计算资源消耗和诊断时间增加的问题，提出对称点模式与改进增强卷积神经网络(efficient channel attention- ConvNeXt, ECA-ConvNeXt)结合的故障诊断模型。首先，定义三相“上下桥臂中点间电压”区分同桥臂双管和内侧单管开路故障，并结合A相电流对其他故障类型进行诊断。然后，利用对称点模式将4种信号融合为一张“雪花图”，丰富数据特征的同时降低计算量。最后，引入双尺寸卷积核和高效跨通道注意力机制提升ECA-ConvNeXt模型特征捕获与泛化能力，同时优化激活函数和残差模块堆叠次数，以兼顾故障诊断精度与速度。实验表明，其对包含同桥臂双管和内侧单管开路故障在内的79种单、双开关管开路故障的诊断精度达99.53%，平均测试时间为8.82 ms，实现了故障诊断精度与速度的平衡。 |
| 关键词: 三电平逆变器 故障诊断 对称点模式 卷积神经网络 通道注意力 |
| DOI：10.19783/j.cnki.pspc.250426 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(52207102)；河南省科技攻关项目资助(252102241061)；河南省研究生教育改革与质量提升工程项目资助(YJS2025AL30) |
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| Fault diagnosis of grid-connected inverters based on symmetric-point pattern and ECA-ConvNeXt |
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HAN Sumin1, 2, ZHANG Shuzhi1, 2, LÜ Wenlong1, 2, JIA Jiaoxin3
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1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China; 2. Henan
Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China;
3. Hebei Province Key Laboratory of Distributed Energy Storage and Microgrid (North
China Electric Power University), Baoding 071003, China
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| Abstract: |
| To solve the difficulty in diagnosing open-circuit faults in same-arm double-switch and inner single-switch in grid-connected neutral point clamped inverters, as well as the increased computational load and diagnosis time caused by excessive detection signals, a fault diagnosis model combining symmetric-point pattern and enhanced convolutional neural network (ECA-ConvNeXt) is proposed. First, the three-phase “midpoint voltages between upper and lower bridge arms” are defined to distinguish same-arm dual-switch faults from inner single-switch open-circuit faults, while A-phase current is used to diagnose other fault types. Then, four types of signals are fused into a single “snowflake diagram” using the symmetric-point pattern, enriching data features while reducing computational complexity. Finally, dual-size convolution kernels and an efficient cross-channel attention mechanism are introduced to improve the feature acquisition and generalization ability of the ECA-ConvNeXt model. At the same time, the activation function and residual module stacking are optimized to balance diagnosis accuracy and speed. Experiments show that the proposed method achieves a diagnosis accuracy of 99.53% for 79 types of single- and double-switch open-circuit faults, including same-arm double-switch and inner single-switch faults, with an average test time of 8.82 ms, effectively balancing accuracy and speed. |
| Key words: three-level inverter fault diagnosis symmetric-point pattern convolutional neural network channel attention |
