| 陈晓旭,刘素梅,刘若溪.计及燃弧频次的配电网单相接地故障分类与辨识[J].电子测量与仪器学报,2024,38(7):236-246 |
| 计及燃弧频次的配电网单相接地故障分类与辨识 |
| Fault classification and identification for single-phase grounding faults in distribution network considering the arcs’ occurrence frequencies |
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| DOI： |
| 中文关键词: 燃弧频次 故障分类与辨识 单相接地故障 零序电流 波形特征 |
| 英文关键词:arcs’ occurrence frequency fault classification and identification single-phase grounding fault zero-sequence current waveforms characteristics |
| 基金项目:国家自然科学基金（52107069）项目资助 |
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| 摘要点击次数: 26 |
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| 中文摘要: |
| 尽管采用小电流接地方式的配电网发生单相接地故障时短路电流较小，但若故障发生后燃弧现象长时间存在，将会显著增加火灾发生风险。为减小火灾隐患，已有多数研究从燃弧现象发生与否角度对故障进行辨识，但并未计及燃弧频次的影响。为此，针对某地配电网实际单相接地故障案例，通过系统分析不同故障场景下零序电流变化特性与燃弧之间的关联规律，从火灾隐患程度角度提出了计及燃弧频次的单相接地故障分类方法；进一步挖掘出不同种类故障发生后零序电流波形分别呈现“平肩畸变”、“瞬态值”等特征，并利用不同频带能量占比、谐波重心和燃弧周期数对波形畸变特征进行了数学描述；以前述数学特征量作为输入，研究建立了基于长短期记忆（long short-term memory, LSTM）网络的故障种类辨识模型，经某电力公司现场收集的223组现场典型故障案例验证，模型识别准确率达96.4%，可有效识别燃弧故障种类，对于减小森林火灾隐患、节约配电网运维成本意义重大。 |
| 英文摘要: |
| The short-circuit currents distributed from distribution networks with small current grounding mode are relatively little after single-phase grounding faults. However, due to the aforementioned faults the long-term presence of arcing phenomena can increase the potential fire risk. In order to minimize fire threat, the existing methods for identifying fault types are based on whether the arcs occur or not. Whereas, the impact of arcs’ occurrence frequency is not taken into account. Aimed at the problem, on the basis of the actual cases of single-phase grounding faults in a certain distribution network the correlation between zero-sequence current characteristics and corresponding arcing phenomenon is firstly analyzed. And a novel classification method for single-phase grounding faults is proposed involving the arcs’ occurrence frequency. The waveforms characteristics of zero-sequence current are further extracted under the distinct fault types, such as “flat shoulder distortion”, “transient change” and so on. The aforementioned characteristics are mathematically described using the energy proportion of zero-sequence current components with different frequency bands, harmonic centroid and the arcing cycle number. Used these mathematical features as inputs, a fault-type identification model based on long short-term memory (LSTM) networks was developed. At last, the proposed model is tested with a dataset of 223 typical fault cases collected from a certain power company. It is verified that the accuracy rate of proposed model is 96.4%. The distinct fault types can be identified effectively. It is significant for reducing the fire risk and saving on the costs associated with the maintenance of distribution networks. |
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