Bad Data Detection Algorithm for PMU Based on Spectral Clustering

Phasor measurement units(PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.

Doubly-fed Deep Learning Method for Bad Data Identification in Linear State Estimation

With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation,which can:(1)identify bad data under both steady states and contingencies;(2)achieve higher accuracy than conventional pre-filtering approaches;(3)reduce iteration burden for linear state estimation;(4)efficiently identify bad data in a parallelizable scheme.The proposed method consists of four key steps:(1)preprocessing filter;(2)online training of short-term deep neural network;(3)offline training of long-term deep neural network;(4)a decision merger.Through delicate design and comprehensive training,the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information.An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method.Multiple test scenarios are applied,which include steady states,three-phase-to-ground faults with(un)successful auto-reclosing,low-frequency oscillation,and low-frequency oscillation with simultaneous threephase-to-ground faults.The proposed method demonstrates satisfactory performance during both the training session and the testing session.

Enhanced Denoising Autoencoder-aided Bad Data Filtering for Synchrophasor-based State Estimation

Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS)applications.However,an increasing number of data quality concerns have been reported,among which bad data can significantly undermine the performance of LSE and many other WAMS applications it supports.Bad data filtering can be difficult in practice due to a variety of issues such as limited processing time,non-uniform and changing patterns,and etc.To pre-process phasor measurement unit(PMU)measurements for LSE,we propose an improved denoising autoencoder(DA)-aided bad data filtering strategy in this paper.Bad data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder module.Two characteristics distinguish the proposed methodology:1)The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency,making it suited for real-time processing;2)the system not only identifies bad data but also recovers it,especially for critical measurements.We use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.

Non-linear state recovery in power system under bad data and cyber attacks

The problems of recovering the state of power systems and detecting the instances of bad data have been widely studied in literature.Nevertheless,these two operations have been designed and optimized for the most part in isolation.Specifically,state estimators are optimized based on the minimum mean-square error criteria,which is only optimal when the source of distortions in the data is Gaussian random noise.Hence,the state estimators fail to perform optimality when the data is further contaminated by bad data,which cannot necessarily be modeled by additive Gaussian terms.The problem of power state estimation has been studied extensively.But the fundamental performance limits and the attendant decision rules are unknown when the data is potentially compromised by random bad data(due to sensor failures)or structured bad data(due to cyber attacks,which are also referred to false data injection attacks).This paper provides a general framework that formalizes the underlying connection between state estimation and bad data detection routines.We aim to carry out the combined tasks of detecting the presence of random and structured bad data,and form accurate estimations for the state of power grid.This paper characterizes the optimal detectors and estimators.Furthermore,the gains with respect to the existing state estimators and bad data detectors are established through numerical evaluations.

ISODATA方法在配网状态估计不良数据辨识中的应用

采用ISODATA技术 (迭代自组织数据分析技术A) ,利用标准残差Rn 和相邻采样时刻量测值之差ΔZ作为特征值 ,对量测数据进行模糊聚类分析 ,并根据隶属度的大小来辨识其是否属于不良数据 .数字仿真表明 ,该计算方法简单、快速、可靠 .

考虑PMU数据质量问题的电力系统扰动检测方法

快速准确的电力系统扰动检测能够为后续扰动分析提供有效的指导信息,而广域测量系统(wide area measurement system,WAMS)的广泛应用为扰动检测提供了有力的数据基础。基于PMU量测数据,该文提出一种考虑PMU不良数据的扰动事件检测方法。首先分析PMU异常数据行为特性,揭示扰动事件与不良数据的差异性特征。进一步,提出一种基于差分Teager-Kaiser能量算子与3Sigma准则相结合的PMU异常数据初筛方法,避免了低强度扰动漏检和扰动的重复检测问题。接着,利用动态时间规整和最大互信息系数分别计算不同PMU间的时空相似性,以及同一台PMU内不同量测间的相关性,并以此作为表征扰动事件和不良数据差异的特征。最后,通过局部离群概率算法对得到的综合度量指标进行分析,可实现在含有不良数据场景下的扰动事件准确检测。基于IEEE39系统,实际电网模型以及PMU实测数据,验证所提方法具有较好准确性、实时性以及泛化能力。

基于最大流算法的电力负荷坏数据辨识系统

用电负荷坏数据会对电力供电产生很大的破坏性,为了提高对电力负荷坏数据的检测能力,设计基于最大流算法的电力负荷坏数据辨识系统。硬件主要包括采集器、处理器、信号识别器三部分,采集器在高压电池侧边需要放置微控制单元,处理器将波形数据转换为数字信息实现控制。信息识别器的连接结构为阅读芯片与单片机的输入端口互相连接,单片机的输出端口与通信状态显示屏相互连接,载波发生器与调制器相互连接。利用最大流算法建立拓扑模型,在电力系统网络流中,采用中心性指标完成对点到边、边到点、点到点、边到边之间的电力数据划分,利用最大流算法对坏数据完成辨识。实验结果表明,所设计系统可以确保电网受到攻击后剩余流量高于95%,辨识准确率高于90%。

基于数字孪生的柔性直流线路保护中不良数据自纠错方法

直流线路保护是柔性直流电网发展的关键,需要在3 ms内完成故障判别。现有学者提出基于数字孪生的柔性直流线路保护方法,具有速度快、灵敏性高等优点,但其可靠性易受互感器测量异常影响,可能导致保护误动作。现有不良数据检测方法的准确性和快速性难以满足直流控制保护设备的需求,因此为提高该保护方法的可靠性,文中提出一种基于移动平均法的不良数据自纠错方法。根据测量数据平稳变化的时序特性,利用移动平均法得到测量数据的预测值,通过比较预测误差与实际误差进行不良数据的检测与纠错,无需迭代计算和预先训练模型。利用四端柔性直流电网进行仿真检验,结果表明相较于已有方法,所提方法具有更好的准确性与快速性,纠错性能较好,能适配保护方法并提升抗干扰能力,有效提高保护的可靠性。

A NEW METHOD OF BAD POINTS ELIMINATION BASED ON HOUGH TRANSFORM

In experimental tests, besides data in range of allowable error, the experimenters usually get some unexpected wrong data called bad points. In usual experimental data processing, the method of bad points exclusion based on automatic programming is seldom taken into consideration by researchers. This paper presents a new method to reject bad points based on Hough transform, which is modified to save computational and memory consumptions. It is fit for linear data processing and can be extended to process data that is possible to be transformed into and from linear form; curved lines, which can be effectively detected by Hough transform. In this paper, the premise is the distribution of data, such as linear distribution and exponential distribution, is predetermined. Steps of the algorithm start from searching for an approximate curve line that minimizes the sum of parameters of data points. The data points, whose parameters are above a self-adapting threshold, will be deleted. Simulation experiments have manifested that the method proposed in this paper performs efficiently and robustly.

基于同步相量测量的配电网状态估计方法

为保证现代电力系统配电网的安全稳定运行和供电能力,需要快速获取配电网准确的实时状态进而对系统进行动态分析、评估和控制。本文构建了复数域下基于同步相量测量的配电网线性状态估计模型,考虑节点注入约束提升了测量的冗余性,分析同步相量测量的误差特点并提出复数域最小二乘法求解状态估计问题。面向实际应用需求,通过分析测量残差提出一种权重自适应调整算法,以及一种不良数据辨识和校正的方法。仿真结果表明,所提方法能有效提高配电网状态估计的准确性、实时性和适用性。

基于深度学习的信息传输过程不良数据辨识模型

针对传统辨识模型存在的耗时长、误差大的问题,本文研究了基于深度学习的信息传输过程不良数据辨识模型。应用卷积神经网络模型对数据样本展开分类,应用深度置信网络模型对分类结果实施反馈微调。在此基础上,设计网络训练和数据聚类过程,通过设置最佳阈值来保证聚类稳定性和辨识精度,从而实现对信息传输过程中不良数据的有效辨识。实验结果表明相比于传统模型,该模型的辨识过程耗时较少,且辨识误差更低,证明其具有更高的应用价值。

基于分层聚合的电力系统不良数据自动辨识

为了准确分辨电力系统中的不良数据,从而保证电网安全运行,提出了基于分层聚合的电力系统不良数据自动辨识方法。根据电力系统主电路连接形式,计算分层标准与聚合指标的实际数值,完成基于分层聚合的电力系统规划。在此基础上,确定电力数据的不良程度,通过数据加权残差的处理方式,得到具体的辨识标度值,利用该值自动辨识电力系统不良数据。对比实验结果显示,与前推回代追踪辨识方法相比,基于分层聚合的自动辨识方法的辨识准确率较高,能够保证电网安全运行,符合实际应用需求。

基于大数据抓取的供应商不良行为数据监测方法

传统供应商选择弊端较为明显,为此,提出基于大数据抓取的供应商不良行为数据监测方法。选取大数据抓取技术——网络爬虫技术获取供应商行为数据,按照时间先后顺序将供应商行为数据转换为平稳时间序列,利用拉格朗日插值算法补全行为数据的缺失,构建ARIMA模型,估计模型参数数值,确定不良行为数据辨识空间,通过拟合残差序列比较获取供应商不良行为数据辨识结果,实现不良行为数据的监测。通过实验数据可知,应用提出方法能够准确地辨识出多种类型的不良行为数据,证明提出方法具备更好的监测性能。

中国煤炭行业绿色全要素生产率研究

绿色全要素生产率(GTFP)是将能源消耗与环境污染同时纳入传统TFP核算框架的兼顾经济增长与环境保护的绿色发展指标。与传统全要素生产率(TFP)相比,其更能反映创新驱动的本质。科学测算煤炭行业GTFP能客观描述科技进步与创新、规模收益等对产出增长的驱动/阻碍情况,对煤炭行业实现高质量发展、助力实现“双碳”目标有重要意义。然而,现有煤炭行业GTFP测算较少将经济、能源与环境因素同时考虑在内,导致测算的GTFP与实际情况有一定的差距。将能源消费与“三废”排放同时纳入研究框架,首先应用DEA分析了2014—2018年中国不同区域所属25个省区市煤炭行业的平均综合技术效率,在此基础上,采用Global-Malmquist-Luenberger指数,测算出了2002—2018年的GTFP,并按区域与时间维度对GTFP进行分解,结果表明:我国煤炭行业平均综合技术效率水平较低,西部地区整体优于东部,东部优于中部,中部优于东北;促进我国煤炭行业GTFP改进的主要原因是技术进步,而不是技术效率,纯技术效率与规模效率是导致技术效率降低的主因,但前者的阻碍作用更大,纯技术增长是技术进步的源泉;分地区看,东部煤炭行业的GTFP变化指数均值(1.021)>中部(1.009)>西部(1.000)>东北(0.963),各地区导致GTFP改进/降低的原因不同;分时间看,考察期内我国煤炭行业GTFP年均增长率为0.2%,主要是技术进步年均增长0.9%,技术效率年均降低0.8%导致的。结合研究结果,提出了提高煤炭行业综合技术效率水平、增强煤炭企业科技创新能力、完善煤炭企业生产管理与制度等对策建议。

基于非线性优化算法的电力系统状态估计技术研究

海上油田电网的安全运行直接影响着我国海上油气生产企业的稳定运行,通过了解状态估计的功能和研究现状,结合海洋石油生产电网的实际情况,提出了一种基于非线性优化算法的状态估计算法。基于状态估计的基本原理和流程,包括目标函数、约束条件的建立以及原-对偶内点算法的求解,采用了状态估计中不良数据检测与识别的计算方法,通过软件模块程序实现状态估计结果的求解。结合实际算例对比分析,得出了在正常情况下、不检测不良数据情况下、系统分裂运行情况下的状态估计结果与系统状态,为实现电力系统状态估计提供了高可靠性、高可信的数据,为电力系统调度中心电网运行状态分析提供参考。

基于子空间跟踪算法的盲虚假数据攻击

虚假数据注入攻击是电力系统网络安全领域的热门课题。当电网保持稳定时,盲虚假数据注入攻击在未知电网雅可比矩阵的情况下,依旧能绕过坏数据检测系统,诱导系统状态估计出错进行错误的调度,从而破坏电网的稳定安全,但该算法在估计雅可比矩阵时,存在运算复杂度高、耗时长的问题。为此,提出了基于紧缩投影逼近子空间跟踪算法的新盲虚假数据攻击算法,以递推更新的方式构造出近似的雅可比矩阵。仿真试验表明:在IEEE 14系统上新算法运算时间仅为传统主成分分析法的1/2,随着电网规模增大这个数字还会下降。所提出的算法较传统算法简化了运算量,提升了攻击成功率。

大数据技术在应收账款管理中的应用研究

随着大数据、云计算、云会计等技术的发展,财务工作已经逐渐进入智能化时代,财务共享应运而生,已经成为集团企业进行日常财务工作管理的重要手段。近年来,部分集团公司试图通过建立财务共享中心以自动处理各子公司的繁杂的日常业务,并制定一套规范的流程以进行管理,从而降低成本,但财务共享模式下应收账款管理中仍然存在着集成度与自动化程度不足的问题,而大数据技术的发展将有力地解决上述问题,该文研究企业日常应收账款管理工作中如何实际应用K-Means算法、人工神经网络中的反向传播算法(Backpropagation Algorithm,BP算法)结合机器人流程自动化(Robotics Process Automation,RPA)技术,借此帮助企业更好地管理其应收账款,满足其经营管理的需求。

基于BP神经网络的电力系统运行不良数据辨识方法

针对现有辨识方法在对电力系统运行不良数据辨识时,存在辨识速度慢、辨识精准度低的问题,引入BP神经网络,开展对辨识方法的设计研究。采集电力系统的运行数据,利用BP神经网络,提取不良数据特征。根据不良数据的特征,通过支持向量机分类算法完成对不良数据的辨识,并通过试验验证该方法的辨识速度和表示精准度。

基于量测数据相关性的电力系统不良数据检测和辨识新方法

电力系统量测数据的质量是影响电力系统状态估计效率和结果的重要因素,而量测数据中客观地存在少量不良数据,检测和辨识这些不良数据是电力系统状态估计的重要组成部分。文章分析了量测数据协方差矩阵中的元素值在量测数据中含有白噪声、突变量和不良数据时的变化规律,提出了通过量测数据协方差矩阵中元素的变化规律检测和辨识不良数据的新方法,在IEEE14节点系统上的仿真试验验证了该方法的正确性。

电力系统不良数据检测与辨识方法的现状与发展

电力系统不良数据的存在会降低电力系统状态估计的收敛性能,甚至造成电力系统状态估计的失败。回顾了国内外对电力系统不良数据检测与辨识方法的历史现状与发展,从对检测和辨识不良数据的各种方法研究的时间顺序以及类别上出发,将其分成基于传统方法和基于新理论方法这两大类并对其分别进行了详细阐述,分析了各个算法的自身特点以及存在的问题,并对该领域值得进一步研究的问题和方向进行了展望。