Model reduction based on limited‐time interval impulse response Gramians

In this paper, three new Gramians are introduced namely ‐ limited‐time interval impulse response Gramians (LTIRG), generalized limited‐time Gramians (GLTG) and generalized limited‐time impulse response Gramians (GLTIRG). GLTG and GLTIRG are applicable to both unstable systems and also to systems which have eigenvalues of opposite polarities and equal magnitude. The concept of these Gramians is utilized to develop model reduction algorithms for linear time‐invariant continuous‐time single‐input single‐output (SISO) systems. In the cases of GLTIRG and GLTG based model reduction, the standard time‐limited Gramians are generalized to be applied to unstable systems by transforming the original system into a new system which requires the solution of two Riccati equations. Two numerical examples are included to illustrate the proposed methods. The results are also compared with standard techniques.     

基于感知哈希序列的电压暂降事件同源识别

针对现有方法未能充分利用电压采样值信息且对特殊录波情况适应性较差的问题,提出基于感知哈希序列相似性的电压暂降事件同源识别方法。首先,提出一种基于突变点检测和录波情况识别的暂降数据段提取方法,将对录波文件中暂降事件的识别转化为对完整和不完整暂降数据段的分别识别。其次,利用格拉姆角场(Gramian angular field, GAF)将提取后的暂降数据段从一维时间序列形式转化为二维图像,并利用感知哈希算法将其转化为哈希序列。然后,通过欧氏距离刻画相似性,并根据选取的阈值完成电压暂降同源识别。最后,利用IEEE30节点系统生成的仿真数据以及北京、福建地区的实测数据进行验证,证明了该方法具有识别精度高、适应性好等优势。     

非侵入式负荷识别的电流序列可视化方法

以单一特征为标签的用电设备识别,因特征携带的信息量不足,在区分性质相似的负荷时易产生误判,为此,提出一种将电流序列编码为图像的二维可视化方法,通过计算机视觉技术对负荷进行分类识别。利用Fryze功率理论提取电流的非有功分量,通过格拉姆角场(GAF)将一维电流序列转换成二维图像,借助数据扩充的方式进行升维,并赋予矩阵颜色特征来提升负荷标签的辨识度;基于迁移学习的思想,利用预训练模型Inception_v3提取并学习GAF图像特征,并以该特征为标签对负荷类型进行分类识别。在2个公开数据集上的实验验证了所提方法在高频采集场景下的准确性和有效性。     

Identification and data-driven model reduction of state-space representations of lossless and dissipative systems from noise-free data

We illustrate procedures to identify a state-space representation of a lossless or dissipative system from a given noise-free trajectory; important special cases are passive systems and bounded-real systems. Computing a rank-revealing factorization of a Gramian-like matrix constructed from the data, a state sequence can be obtained; the state-space equations are then computed by solving a system of linear equations. This idea is also applied to perform model reduction by obtaining a balanced realization directly from data and truncating it to obtain a reduced-order model.     

An optimal location of static VAr compensator based on Gramian critical energy for damping oscillations in power systems

The increase in the scale and complexity of interconnected power systems leads to multiple electromechanical oscillations. Therefore, the electric network needs to be made more stable. One type of Flexible alternating current transmission systems (FACTS) devices, namely the static VAr compensator (SVC), can be installed at buses to increase the stability margins and dampen the power system oscillations by exchanging capacitive and/or inductive load to maintain and/or control specific parameters of electrical power systems. The efficiency and performance of the SVC depend on its optimal location. Consequently, seeking the optimal placement for SVC has become a key issue. In this paper, we propose a novel method to find the optimal location for the SVC based on a two‐step approach. First, an energetic approach based on the combination of the controllability Gramian critical energy analysis with the balanced realization reduction technique is used to search for several feasible locations; then the transient stability is analyzed to compare and determine an optimal location through various test cases. The effectiveness of the proposed method has been demonstrated on the IEEE 39‐bus England power system and compared with the modal controllability index. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

经验Gramian平衡降阶在电力系统中的改进及应用

经验Gramian平衡降阶方法正逐步应用于电力系统的非线性控制设计。如何获得有效经验可观可控Gramian矩阵,是该方法应用的核心,对降阶效果有着很大影响。为进一步提升该方法的降阶效果,建立了电力系统非线性动态模型,分析了所研究问题与控制量选取及降阶对象之间的关系。考虑了电力系统状态量及控制量变化特点,提出了一种方案用于确定各状态量和控制量的扰动值,形成了包含丰富系统动态行为信息的经验可观可控Gramian矩阵。通过研究某实际电网验证该方案可行有效,研究表明相比原方法,所提方案在保证降阶误差上界的前提下,能够进一步有效减少模型阶数,其降阶效果得到明显提升。     

Series arc fault diagnosis method of photovoltaic arrays based on GASF and improved DCGAN

In recent years, the methods of machine learning are widely investigated to resolve the series arc fault (SAF) diagnosis problem in photovoltaic (PV) arrays. However, owing to the factors such as weak signal characteristics, long algorithm execution time, and sample imbalance in practical applications, these methods may have difficulties of detecting the SAF. To address these problems, a method based on the Gramian angular summation field (GASF) combined with the squeeze and excitation-deep convolution generative adversarial network (SE-DCGAN) is proposed. Firstly, the absolute difference of margin factor (ADMF) of the current signal is calculated to accurately extract the transient current data when the SAF occurs. Thereafter, the GASF is used to convert transient current data into two-dimensional images to amplify the universal characteristics of the SAF. Subsequently, the SE-DCGAN is adopted to augment the GASF images of the SAF to solve the problem of limited SAF samples. Finally, a convolutional neural network (CNN) is trained to identify the SAF. Also, a fusion sample training method is proposed in this research, that is, normal samples of different PV systems are added to the training set to enhance the generalization ability of CNN. The advantages of the proposed method are that the identification of SAF is improved by converting one-dimensional signals into two-dimensional images, and the generalization ability of the detection model is improved by exploiting the common features of SAFs and fusion training. The validity and generalization ability of the proposed method are verified by three datasets under different PV systems. Experimental results reveal that the proposed method can achieve high recognition accuracy for the measured data; moreover, no misjudgments occurred in identifying the interference events such as maximum power point tracking (MPPT) adjustment and irradiance mutation (IM). In addition, the experiments confirm that the fusion training method enables the model more universal and applicable.     

A notion of possible controllability for uncertain linear systems with structured uncertainty

This paper introduces a notion of possible controllability for a class of uncertain linear systems with structured uncertainty described by averaged integral quadratic constraints. This notion relates to the question of when a state is controllable for some possible value of the uncertainty. The notion of possible controllability is motivated by a desire to extend the theory of minimal realization for linear time invariant systems to the case of uncertain systems with structured uncertainty.     

基于格拉姆角场和CNN-RNN的滚动轴承故障诊断方法

针对卷积神经网络难以处理时间序列数据和循环神经网络难以提取数据深层特征的问题,提出了一种基于深度卷积网络和循环神经网络相结合的滚动轴承故障诊断方法.首先,使用格拉姆角场(GAF)编码将一维轴承振动信号构造为时序图像并划分为训练集、验证集和测试集;然后,将训练集和验证集输入VGG16模型进行特征提取,将提取到的特征输入R...