Electrically conducting fabrics used as flexible sensors can be produced by vacuumed vapor deposition.The research of what impacts the wide and reliable application of the flexible sensors shows that the stability of ...Electrically conducting fabrics used as flexible sensors can be produced by vacuumed vapor deposition.The research of what impacts the wide and reliable application of the flexible sensors shows that the stability of flexible sensors is one of the critical issues.The electrical performance of weft knitted fabrics in course and wale direction does not show significant differences under different ambient conditions,which include time,temperature,and relative humidity.Tests of stimuli responding sensitivity of conductive fabrics indicate that the sensitivity keeps at a constant level and the average sensitivity is stable over 38 days.展开更多
针对大规模风电场接入给电力系统安全稳定分析过程带来的方程高维性、计算效率低问题,提出了一种基于模型-数据混合驱动的风电场暂态过程等值方法。对整个风电场进行两阶段聚合,最终将含有多台风机的风电场等值为单台风机模型。第一阶...针对大规模风电场接入给电力系统安全稳定分析过程带来的方程高维性、计算效率低问题,提出了一种基于模型-数据混合驱动的风电场暂态过程等值方法。对整个风电场进行两阶段聚合,最终将含有多台风机的风电场等值为单台风机模型。第一阶段等值过程中基于物理模型驱动思想,通过获取不同风速下风机并网点有功暂态响应特性及出力特性,采用K-means方法将所有风机聚合为四台等效风机。第二阶段基于数据驱动策略,通过所建立风电场并网点有功偏差最小的多时段优化模型并结合长短期记忆网络(long short term memory,LSTM)训练,将四台等效风机聚合为单台风机模型,实现了整个风电场的暂态过程等值聚合。通过算例验证了所提等值方法能够有效地反映风电场的暂态过程特性,为电力系统安全稳定分析模型降维提供了一种实用化解决方案。展开更多
文摘Electrically conducting fabrics used as flexible sensors can be produced by vacuumed vapor deposition.The research of what impacts the wide and reliable application of the flexible sensors shows that the stability of flexible sensors is one of the critical issues.The electrical performance of weft knitted fabrics in course and wale direction does not show significant differences under different ambient conditions,which include time,temperature,and relative humidity.Tests of stimuli responding sensitivity of conductive fabrics indicate that the sensitivity keeps at a constant level and the average sensitivity is stable over 38 days.
文摘针对大规模风电场接入给电力系统安全稳定分析过程带来的方程高维性、计算效率低问题,提出了一种基于模型-数据混合驱动的风电场暂态过程等值方法。对整个风电场进行两阶段聚合,最终将含有多台风机的风电场等值为单台风机模型。第一阶段等值过程中基于物理模型驱动思想,通过获取不同风速下风机并网点有功暂态响应特性及出力特性,采用K-means方法将所有风机聚合为四台等效风机。第二阶段基于数据驱动策略,通过所建立风电场并网点有功偏差最小的多时段优化模型并结合长短期记忆网络(long short term memory,LSTM)训练,将四台等效风机聚合为单台风机模型,实现了整个风电场的暂态过程等值聚合。通过算例验证了所提等值方法能够有效地反映风电场的暂态过程特性,为电力系统安全稳定分析模型降维提供了一种实用化解决方案。