电网规模的扩大使得电力系统运行状态变得更加复杂,对电网安全稳定运行提出了更高要求。提出了基于深度学习中长短时记忆(long-and-short term memory,LSTM)的电力暂态稳定在线评估模型。该模型通过获取全网各节点电压、电流、功率等电...电网规模的扩大使得电力系统运行状态变得更加复杂,对电网安全稳定运行提出了更高要求。提出了基于深度学习中长短时记忆(long-and-short term memory,LSTM)的电力暂态稳定在线评估模型。该模型通过获取全网各节点电压、电流、功率等电气量,实时计算得到电网失稳可能性评分,并在新英格兰10机39线系统上对该模型进行测试与优化。实验结果表明,该模型能通过实时运算得到电网稳定性的评估及预警,具有准确性高、预警能力强、支持在线监测的特点。展开更多
Induction motors （IMs） are commonly used in various industrial applications. To improve energy con- sumption efficiency, a reliable IM health condition moni- toring system is very useful to detect IM fault at its ea...Induction motors （IMs） are commonly used in various industrial applications. To improve energy con- sumption efficiency, a reliable IM health condition moni- toring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is pro- posed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are syn- thesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air- gap eccentricity diagnosis. The effectiveness of the pro- posed harmonic synthesis technique is examined experi- mentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.展开更多
Continuous monitoring of wind turbine(WT)opera-tion can improve the reliability of the wind turbine and lower the operation and maintenance costs.To improve the condition mon-itoring(CM)and fault detection performance...Continuous monitoring of wind turbine(WT)opera-tion can improve the reliability of the wind turbine and lower the operation and maintenance costs.To improve the condition mon-itoring(CM)and fault detection performance on WTs,this paper proposes an artificial intelligence-based probabilistic anomaly detection approach that can not only provide a deterministic estimation of the WT condition but also evaluate the uncertainties associated with the estimation.An abnormal WT condition is detected based on the evaluated uncertainties,to provide a noise-free incipient fault indication.Compared to the conventional deterministic CM approaches with a residual-based anomaly detection criterion,the proposed probabilistic approach tends to accurately detect the faults earlier,which allows more time for maintenance scheduling to prevent WT component failure.The early fault detection ability of the proposed approach was verified on an operational WT in China.展开更多
Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical mod...Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.展开更多
文摘电网规模的扩大使得电力系统运行状态变得更加复杂,对电网安全稳定运行提出了更高要求。提出了基于深度学习中长短时记忆(long-and-short term memory,LSTM)的电力暂态稳定在线评估模型。该模型通过获取全网各节点电压、电流、功率等电气量,实时计算得到电网失稳可能性评分,并在新英格兰10机39线系统上对该模型进行测试与优化。实验结果表明,该模型能通过实时运算得到电网稳定性的评估及预警,具有准确性高、预警能力强、支持在线监测的特点。
基金Supported in part by Natural Sciences and Engineering Research Council of Canada(NSERC)eMech Systems IncBare Point Water Treatment Plant in Thunder Bay,Ontario,Canada
文摘Induction motors （IMs） are commonly used in various industrial applications. To improve energy con- sumption efficiency, a reliable IM health condition moni- toring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is pro- posed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are syn- thesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air- gap eccentricity diagnosis. The effectiveness of the pro- posed harmonic synthesis technique is examined experi- mentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.
基金The work was supported in part by the Australian Research Council(ARC)Discovery Grant(DP170103427/180103217)in part by the Funda-mental Research Funds for the Central Universities(No.2017BSCXB58)and the Postgraduate Research&Practice Innovation Program of Jiangsu Province.
文摘Continuous monitoring of wind turbine(WT)opera-tion can improve the reliability of the wind turbine and lower the operation and maintenance costs.To improve the condition mon-itoring(CM)and fault detection performance on WTs,this paper proposes an artificial intelligence-based probabilistic anomaly detection approach that can not only provide a deterministic estimation of the WT condition but also evaluate the uncertainties associated with the estimation.An abnormal WT condition is detected based on the evaluated uncertainties,to provide a noise-free incipient fault indication.Compared to the conventional deterministic CM approaches with a residual-based anomaly detection criterion,the proposed probabilistic approach tends to accurately detect the faults earlier,which allows more time for maintenance scheduling to prevent WT component failure.The early fault detection ability of the proposed approach was verified on an operational WT in China.
文摘Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.
