随着现代电力系统的迅猛发展,电网结构和运行方式日益复杂,对状态估计的实时性和准确性也提出了更高的要求。为此,该文提出一种基于深度神经网络的电力系统快速状态估计,通过相关性分析筛选出该状态估计模型的输入量测集,进一步利用海...随着现代电力系统的迅猛发展,电网结构和运行方式日益复杂,对状态估计的实时性和准确性也提出了更高的要求。为此,该文提出一种基于深度神经网络的电力系统快速状态估计,通过相关性分析筛选出该状态估计模型的输入量测集,进一步利用海量历史数据建立基于深度神经网络的状态估计模型。当电力系统的实时量测更新时,将强相关量测输入已建立的状态估计模型中快速获得系统状态的估计结果。通过在IEEE标准系统和某实际省网进行算例仿真表明,所提方法的估计精度和鲁棒性均优于传统加权最小二乘(weighted least square,WLS)和加权最小绝对值估计(weighted least absolute value,WLAV);并且该方法的在线计算时间受系统规模影响较小,由实际省网的仿真结果可知,其计算效率较WLS和WLAV分别提升1.43和27.2倍。展开更多
In the past decade,dramatic progress has been made in the field of machine learning.This paper explores the possibility of applying deep learning in power system state estimation.Traditionally,physics-based models are...In the past decade,dramatic progress has been made in the field of machine learning.This paper explores the possibility of applying deep learning in power system state estimation.Traditionally,physics-based models are used including weighted least square(WLS)or weighted least absolute value(WLAV).These models typically consider a single snapshot of the system without capturing temporal correlations of system states.In this paper,a physics-guided deep learning(PGDL)method is proposed.Specifically,inspired by autoencoders,deep neural networks(DNNs)are used to learn the temporal correlations.The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations.Hence,the proposed PGDL is both data-driven and physics-guided.The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases.Simulations show promising results and the applicability is further discussed.展开更多
Several security solutions have been proposed to detect network abnormal behavior. However, successful attacks is still a big concern in computer society. Lots of security breaches, like Distributed Denial of Service(...Several security solutions have been proposed to detect network abnormal behavior. However, successful attacks is still a big concern in computer society. Lots of security breaches, like Distributed Denial of Service(DDoS),botnets, spam, phishing, and so on, are reported every day, while the number of attacks are still increasing. In this paper, a novel voting-based deep learning framework, called VNN, is proposed to take the advantage of any kinds of deep learning structures. Considering several models created by different aspects of data and various deep learning structures, VNN provides the ability to aggregate the best models in order to create more accurate and robust results. Therefore, VNN helps the security specialists to detect more complicated attacks. Experimental results over KDDCUP'99 and CTU-13, as two well known and more widely employed datasets in computer network area, revealed the voting procedure was highly effective to increase the system performance, where the false alarms were reduced up to 75% in comparison with the original deep learning models, including Deep Neural Network(DNN), Convolutional Neural Network(CNN), Long Short-Term Memory(LSTM), and Gated Recurrent Unit(GRU).展开更多
年龄变化是影响人脸识别模型性能的主要原因之一,为解决年龄变化所带来的模型识别率低的问题,提出了一种基于深度学习的跨年龄卷积神经网络模型(CA-CNN)用于跨年龄人脸识别。首先,利用卷积神经网络提取人脸图像中的深度人脸特征;然后,...年龄变化是影响人脸识别模型性能的主要原因之一,为解决年龄变化所带来的模型识别率低的问题,提出了一种基于深度学习的跨年龄卷积神经网络模型(CA-CNN)用于跨年龄人脸识别。首先,利用卷积神经网络提取人脸图像中的深度人脸特征;然后,提出一种高效的卷积注意力模块从深度人脸特征中获取年龄特征,并结合多层感知机和多任务监督学习,将深度人脸特征非线性分解为年龄特征和身份特征;最后,为了更好地区分身份特征和年龄特征,提出了一种批核典型相关性分析模块对分解后的身份特征和年龄特征进行相关性分析。经过对抗性学习训练后,相关性最小化,实现了跨年龄人脸识别。所提模型在MORPH Album 2数据集上的rank-1识别准确率达到了99.03%,在CALFM数据集上的人脸验证等错率为9.8%,表明了所提模型的有效性。展开更多
文摘随着现代电力系统的迅猛发展,电网结构和运行方式日益复杂,对状态估计的实时性和准确性也提出了更高的要求。为此,该文提出一种基于深度神经网络的电力系统快速状态估计,通过相关性分析筛选出该状态估计模型的输入量测集,进一步利用海量历史数据建立基于深度神经网络的状态估计模型。当电力系统的实时量测更新时,将强相关量测输入已建立的状态估计模型中快速获得系统状态的估计结果。通过在IEEE标准系统和某实际省网进行算例仿真表明,所提方法的估计精度和鲁棒性均优于传统加权最小二乘(weighted least square,WLS)和加权最小绝对值估计(weighted least absolute value,WLAV);并且该方法的在线计算时间受系统规模影响较小,由实际省网的仿真结果可知,其计算效率较WLS和WLAV分别提升1.43和27.2倍。
文摘In the past decade,dramatic progress has been made in the field of machine learning.This paper explores the possibility of applying deep learning in power system state estimation.Traditionally,physics-based models are used including weighted least square(WLS)or weighted least absolute value(WLAV).These models typically consider a single snapshot of the system without capturing temporal correlations of system states.In this paper,a physics-guided deep learning(PGDL)method is proposed.Specifically,inspired by autoencoders,deep neural networks(DNNs)are used to learn the temporal correlations.The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations.Hence,the proposed PGDL is both data-driven and physics-guided.The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases.Simulations show promising results and the applicability is further discussed.
基金supported by the National Natural Science Foundation of China (No. 61872212)the National Key Research and Development Program of China (No.2016YFB1000102)。
文摘Several security solutions have been proposed to detect network abnormal behavior. However, successful attacks is still a big concern in computer society. Lots of security breaches, like Distributed Denial of Service(DDoS),botnets, spam, phishing, and so on, are reported every day, while the number of attacks are still increasing. In this paper, a novel voting-based deep learning framework, called VNN, is proposed to take the advantage of any kinds of deep learning structures. Considering several models created by different aspects of data and various deep learning structures, VNN provides the ability to aggregate the best models in order to create more accurate and robust results. Therefore, VNN helps the security specialists to detect more complicated attacks. Experimental results over KDDCUP'99 and CTU-13, as two well known and more widely employed datasets in computer network area, revealed the voting procedure was highly effective to increase the system performance, where the false alarms were reduced up to 75% in comparison with the original deep learning models, including Deep Neural Network(DNN), Convolutional Neural Network(CNN), Long Short-Term Memory(LSTM), and Gated Recurrent Unit(GRU).
文摘年龄变化是影响人脸识别模型性能的主要原因之一,为解决年龄变化所带来的模型识别率低的问题,提出了一种基于深度学习的跨年龄卷积神经网络模型(CA-CNN)用于跨年龄人脸识别。首先,利用卷积神经网络提取人脸图像中的深度人脸特征;然后,提出一种高效的卷积注意力模块从深度人脸特征中获取年龄特征,并结合多层感知机和多任务监督学习,将深度人脸特征非线性分解为年龄特征和身份特征;最后,为了更好地区分身份特征和年龄特征,提出了一种批核典型相关性分析模块对分解后的身份特征和年龄特征进行相关性分析。经过对抗性学习训练后,相关性最小化,实现了跨年龄人脸识别。所提模型在MORPH Album 2数据集上的rank-1识别准确率达到了99.03%,在CALFM数据集上的人脸验证等错率为9.8%,表明了所提模型的有效性。
