Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality.In this paper,an attention-based inde...Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality.In this paper,an attention-based independently recurrent neural network(IndRNN)for sag source location and sag type recognition in sparsely monitored power system is proposed.Specially,the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system,and the desired outputs simultaneously contain the following information:the located lines where sag occurs;the corresponding sag types,including motor starting,transformer energizing and short circuit;and the fault phase for short circuit.In essence,the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible.A favorable feature of the proposed method is that it can be realized without system parameters or models.The proposed method is validated by IEEE 30-bus system and a real 134-bus system.Experimental results demonstrate that the accuracy of sag source location is higher than 99%for all lines,and the accuracy of sag type recognition is also higher than 99%for various sag sources including motor starting,transformer energizing and 7 different types of short circuits.Furthermore,a comparison among different monitor placements for the proposed method is conducted,which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.展开更多
In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining...In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining useful life(RUL) of the equipment can be accurately predicted, the equipment can be maintained in time to avoid the downtime caused by equipment failure and greatly improve the production efficiency of enterprises. This paper aims to use independently recurrent neural network(IndRNN) to learn health degradation of turbofan engine and make accurate predictions of its RUL, which not only effectively solves the problem of gradient explosion and vanishing, but also increases the interpretability of neural networks. IndRNN can be used to process longer time series which matches the scene with high frequency sampling sensor in industrial practical applications. The results demonstrate that IndRNN for RUL estimation significantly outperforms traditional approaches, as well as convolutional neural network(CNN) and long short-term memory network(LSTM) for RUL estimation.展开更多
本文主要是对在线问诊中产生的医疗文本进行命名实体识别的研究.使用在线医疗问答网站的数据,采用{B, I, O}标注体系构建数据集,抽取疾病、治疗、检查和症状四个医疗实体.以BiLSTM-CRF为基准模型,提出两种深度学习模型IndRNN-CRF和IDCNN...本文主要是对在线问诊中产生的医疗文本进行命名实体识别的研究.使用在线医疗问答网站的数据,采用{B, I, O}标注体系构建数据集,抽取疾病、治疗、检查和症状四个医疗实体.以BiLSTM-CRF为基准模型,提出两种深度学习模型IndRNN-CRF和IDCNN-BiLSTM-CRF,并在自构建数据集上验证模型的有效性.将新提出的两种模型与基准模型通过实验对比得出:模型IDCNN-BiLSTM-CRF的F1值0.8116,超过了BiLSTM-CRF的F1值0.8009, IDCNN-BiLSTM-CRF整体性能好于BiLSTM-CRF模型;模型IndRNN-CRF的精确率0.8427,但该模型在召回率上低于基准模型BiLSTM-CRF.展开更多
基金This work was partly supported by National Natural Science Foundation of China(No.61903296)Key Project of Natural Science Basic Research Plan in Shaanxi Province of China(No.2019ZDLGY18-03)+1 种基金Thousand Talents Plan of Shaanxi Province for Young Professionals,Project of Shaanxi Science and Technology(No.2019JQ-329)Doctoral Scientific Research Foundation of Xi’an University of Technology(No.103-451116012).
文摘Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality.In this paper,an attention-based independently recurrent neural network(IndRNN)for sag source location and sag type recognition in sparsely monitored power system is proposed.Specially,the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system,and the desired outputs simultaneously contain the following information:the located lines where sag occurs;the corresponding sag types,including motor starting,transformer energizing and short circuit;and the fault phase for short circuit.In essence,the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible.A favorable feature of the proposed method is that it can be realized without system parameters or models.The proposed method is validated by IEEE 30-bus system and a real 134-bus system.Experimental results demonstrate that the accuracy of sag source location is higher than 99%for all lines,and the accuracy of sag type recognition is also higher than 99%for various sag sources including motor starting,transformer energizing and 7 different types of short circuits.Furthermore,a comparison among different monitor placements for the proposed method is conducted,which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.
基金supported by 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of China “Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks”。
文摘In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining useful life(RUL) of the equipment can be accurately predicted, the equipment can be maintained in time to avoid the downtime caused by equipment failure and greatly improve the production efficiency of enterprises. This paper aims to use independently recurrent neural network(IndRNN) to learn health degradation of turbofan engine and make accurate predictions of its RUL, which not only effectively solves the problem of gradient explosion and vanishing, but also increases the interpretability of neural networks. IndRNN can be used to process longer time series which matches the scene with high frequency sampling sensor in industrial practical applications. The results demonstrate that IndRNN for RUL estimation significantly outperforms traditional approaches, as well as convolutional neural network(CNN) and long short-term memory network(LSTM) for RUL estimation.
文摘本文主要是对在线问诊中产生的医疗文本进行命名实体识别的研究.使用在线医疗问答网站的数据,采用{B, I, O}标注体系构建数据集,抽取疾病、治疗、检查和症状四个医疗实体.以BiLSTM-CRF为基准模型,提出两种深度学习模型IndRNN-CRF和IDCNN-BiLSTM-CRF,并在自构建数据集上验证模型的有效性.将新提出的两种模型与基准模型通过实验对比得出:模型IDCNN-BiLSTM-CRF的F1值0.8116,超过了BiLSTM-CRF的F1值0.8009, IDCNN-BiLSTM-CRF整体性能好于BiLSTM-CRF模型;模型IndRNN-CRF的精确率0.8427,但该模型在召回率上低于基准模型BiLSTM-CRF.
