| 基于深度循环神经网络的换相失败边界检测 |
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| 引用本文： | 王卉,王增平,刘席洋.基于深度循环神经网络的换相失败边界检测[J].现代电力,2019,36(6):88-94. |
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| 作者姓名： | 王卉 王增平 刘席洋 |
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| 作者单位： | 新能源电力系统国家重点实验室(华北电力大学 |
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| 基金项目： | 国家重点研发计划智能电网技术与装备重点专项(2016YFB0900604);国家电网公司科技项目(52094017000);国家自然科学基金重点项目(51637005) |
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| 摘 要： | 交直流混联电网中交流故障导致的换相失败,可能引发交流保护误动、拒动,造成连锁性故障。因此,快速精准的换相失败边界检测对提升交流保护性能、优化交直流保护协同配合、保障电网安全稳定运行具有重要意义。对此,提出了基于深度循环神经网络的换相失败边界检测方法。利用逆变站换流母线三相电压、直流电流及触发角指令实时值等站域信息,实现可综合考虑多因素耦合作用,可准确追溯引发换相失败原因,并含有一定的预测功能的换相失败边界检测新方法。
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| 关 键 词： | 交直流混联电网 深度循环神经网络 换相失败边界检测 多因素 站域信息 |
| 收稿时间： | 2018-11-16 |
Commutation Failure Boundary Detection Based on Deep Recurrent Neural Network |
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| Affiliation: | State Key Laboratory of Alternate Electrical Power System with Renewable Energy SourcesNorth China Electric Power University,Beijing 102206, China |
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| Abstract: | In hybrid AC and DC grid, commutation failures will likely cause protection malfunction and may finally lead to serious cascading failures. Therefore, the accurate commutation failure boundary detection is of great value to improve AC protections’ performance, optimize AC and DC protections’ coordination and ensure power grid’s safety and stability. A new approach for fast commutation failure diagnosis is proposed based on DRNN(Deep Recurrent Neural Network). By using the converter bus voltages, DC current and the trigger angle commands of the inverter, this method can consider multiple factors affecting the commutation process, can trace the cause of commutation failure accurately and has certain predictive function. |
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