山东大学学报 (工学版) ›› 2021, Vol. 51 ›› Issue (4): 124-130.doi: 10.6040/j.issn.1672-3961.0.2020.538
• • 上一篇
廖毅1,罗炜2,蒋峰伟1,李亚锦3,于大洋3*
LIAO Yi1, LUO Wei2, JIANG Fengwei1, LI Yajin3, YU Dayang3*
摘要： 为解决换流站阀冷系统状态缺乏智能预测手段和极端工况下冷却能力是否充裕难以评估的问题,提出基于长短期记忆网络(long short-term memory, LSTM)的换流阀冷却裕度预测方法。在阀冷系统冷却裕度指标量化评估的基础上,考虑多源影响因素,通过相关性强弱选择特征量并构建数据样本集,利用长短时记忆网络建立预测模型,并基于大量实际样本数据进行训练,对入水温度和冷却裕度发展趋势做出预测、提前预警,同时提供极端工况下冷却裕度的分析模型,为现场处理决策提供依据。通过穗东换流站的实例分析,验证了算法的有效性和可行性。
中图分类号:
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