引用本文: | 颜肃,张玮亚,李宏仲,王磊.基于人工智能的输电线路故障快速诊断方法研究[J].电力系统保护与控制,2019,47(19):94-99.[点击复制] |
YAN Su,ZHANG Weiya,LI Hongzhong,WANG Lei.Research on fast fault diagnosis of transmission line based on artificial intelligence[J].Power System Protection and Control,2019,47(19):94-99[点击复制] |
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摘要: |
针对电力系统发生故障后未经处理的多源告警信息可能导致故障处理时间增加的问题,提出一种基于人工智能的输电线路故障诊断方法。对输电线路故障诊断Petri网模型进行改进,在建立的模糊Petri网模型中预设变迁阈值,采用逆向搜索策略,对模糊Petri网进行约简,减小推理规模,提高故障诊断效率。所提方法可以对故障信息进行快速分析、分类处理,大大减少调度员处理信息的工作量,提高输电线路自动化水平,确保电力系统安全稳定运行。通过算例验证了所提方法的有效性。 |
关键词: 人工智能 模糊Petri网 输电线路 故障诊断 |
DOI:10.19783/j.cnki.pspc.181527 |
投稿时间:2018-12-06修订日期:2019-02-05 |
基金项目:国家自然科学青年基金(51407113) |
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Research on fast fault diagnosis of transmission line based on artificial intelligence |
YAN Su,ZHANG Weiya,LI Hongzhong,WANG Lei |
(State Grid Nanjing Power Supply Company, Nanjing 210019, China;College of Electrical Engineering, Shanghai Electric Power University, Shanghai 200090, China;NARI Technology Co., Ltd., Nanjing 211106, China) |
Abstract: |
Aiming at the problem that the unprocessed multi-source alarm information may cause the fault processing time to increase after the failure of the power system, an artificial intelligence-based fault diagnosis method for the transmission line is proposed. It improves the Petri net model of transmission line fault diagnosis, presets the transition threshold in the established fuzzy Petri net model, and uses the reverse search strategy to reduce the fuzzy Petri net, decrease the inference scale and improve the fault diagnosis efficiency. The proposed method can quickly analyze and classify fault information, greatly reduce the workload of dispatchers processing information, improve the automation level of transmission lines, and ensure the safe and stable operation of power systems. The effectiveness of the proposed method is verified by an example. This work is supported by National Natural Youth Science Foundation of China (No. 51407113). |
Key words: artificial intelligence Petri net improvement transmission line fault diagnosis |