POSTPROCESSING OF ELECTRIC POWER QUALITY DATA

ABSTRACT

System measurements made to assess power quality in a power system often result in a large volume of data. This is the case because, for a three phase system, three voltages and currents are sampled and even for modest time windows, the number of stored points can be very large. In this paper, post-measurement techniques are suggested to process the measured data. The main post-processing methods are related to probabilistic indices of power quality. The calculation of probability of compliance with the IEEE Standard 519 is discussed. The confidence of these calculations is also discussed. A deconvolu-tion method is demonstrated for calculating missing data.     

基于射频识别技术的电能质量计量评估模型

为了解决传统方法中未考虑多参量指标对电能质量的影响、电能质量计量结果准确度较低等问题,提出基于射频识别技术的电能质量计量评估模型。利用射频识别技术采集电能质量EPC码,获取电能质量计量评估数据,并以电流总谐波畸变率、三相不平衡和频率偏差等为计量评估指标进行权重计算,获取各项指标综合权重值;引入雷达图分析方法,比较电能质量的多变量,定义综合性的评估函数,反映电能质量情况,实现电能质量综合性评估。以评估稳定性和评估准确性为实验指标的实验结果显示,该模型可实现电能质量的高精度评估,实际应用的可靠性较强。     

基于APSI多级策略下的混合电力系统状态估计研究

针对混合电力系统中要求状态估计数据精度高及数据计算量小进行探索研究。提出了两种状态估计构架用于多级策略下SCADA/PMU混合量测的电力系统状态估计。第一个构架是利用先验状态信息(APSI)和正交分块吉文斯旋转提高基于SCADA/PMU量测装置的状态估计的数据处理能力。第二个构架是一个依赖于融合估计的三级方案,优化之前已经确定的SCADA/PMU估计量测值。对多级策略在计算量和精确性上的优化进行详细的描述和比较。在IEEE 30节点系统测试系统进行仿真验证,验证了提出的多级策略能有效地减少状态估计的计算量及增加量测数据的同步性和准确性。     

Voltage stability monitoring of power systems using reduced network and artificial neural network

This paper presents network reduction based methodologies to monitor voltage stability of power systems using limited number of measurements. In a multi-area power system, artificial neural networks (ANNs) are used to estimate the loading margin of the overall system, based on measurements from the internal area only. Information regarding the important measurements from the external areas is considered in measurement transformation through the network reduction process, to enhance the estimation accuracy of the ANNs. A Z-score based bad or missing data processing algorithm is implemented to make the methodologies robust. To account for changing operating conditions, adaptive training of the ANNs is also suggested. The proposed methods are successfully implemented on IEEE 14-bus and 118-bus test systems.     

An incremental measurement placement algorithm for state estimation

Measurement configuration and redundancy significantly impact the quality of state estimation solutions. Practical and efficient algorithms are needed to suggest additional measurement locations in order to increase local redundancy and strengthen network observability. In this paper, an incremental measurement placement algorithm is proposed to enhance the quality of state estimation solutions. The algorithm identifies buses with low accuracy based on state estimation error variances. A list of candidate measurements is suggested to reduce these variances. Certain heuristics are applied to discourage introduction of the possible leverage point measurements. The algorithm has been implemented in the PG&E EMS environment and tested using the real-time data     

基于广域测量系统的状态估计研究综述

广域测量系统（WAMS）的逐步发展给电力系统在线分析方法提供了一个新思路。针对WAMS测量精度高、具有同步相量测量功能以及数据传输快等特点，分别从引入高精度节点电压相量量测的状态估计算法、引入高精度支路电流相量量测的算法、引入全部WAMS量测的算法以及其他与WAMS状态估计相关的问题等4个方面，介绍了目前引入WAMS量测的各种状态估计算法；并详细分析了各种算法的优缺点和适用范围，从工程应用出发研究其可行性，对部分算法给出了改进措施。讨论了WAMS的不良数据检测与辨识问题、相量测量装置（PMU）的最优配置问题以及基于PMU的动态状态估计和谐波状态估计等与WAMS状态估计相关的其他问题。     

Present and Future Trends and Needs in Electric Power Quality Sensors and Instrumentation

Sensors and instrumentation systems form an important part of power quality assessment and preservation. In this paper, the present and future trends in power quality instrumentation are discussed and the present capabilities are identified (e.g., bandwidth, dynamic range, quantities measured). Innovative methods of instrumenting nonsinusoidal voltages and currents are described, including applications of the Global Positioning System (GPS), solid state sensors, sensors that involve light and video, advanced data compression techniques, and advanced state estimation. The cost-to-benefit ratio of sensor development is evaluated in the context of the deregulated electric utility business environment.     

基于大数据分析的暂态电能质量综合评估方法

运用基于大数据处理架构的Naive Bayes分类方法提出了暂态电能质量评估方法,将数据来源扩展至电网运行监测数据、电力用户数据和公共信息数据等方面,并将评估结果按严重程度分为暂态正常状态、短时电压暂降状态、短时深度电压暂降状态、短时电压失压状态。基于MapReduce架构,设计分布式Naive Bayes算法实现状态分类。在分类器训练阶段,对海量历史数据进行分布式学习,周期性地生成评估规则库并部署到所有评估节点。在状态评估阶段,各评估节点基于流处理框架快速生成实时评估样本,并根据当前规则库实时地得出评估结果。试验结果表明,所提出的基于大数据分析的暂态电能质量评估方法是可行,在准确率和处理速度上都取得了较好的效果。     

微波功率测量中的小信号检测

现代微波功率测量仪器大多采用基于微波二极管检波方式的微波功率传感器进行功率测量。二极管功率传感器具有功率测量动态范围大、灵敏度高的优点,但在进行微波小信号检测时输出电压非常小,受噪声影响大,非常难以检测。分析了微波功率传感器中影响小信号检测的噪声来源,针对这些影响小信号功率测量的噪声,从检波电路、斩波电路以及数据采集电路等方面综合进行设计,有效地提高了微波小信号的功率测量灵敏度和准确度。应用于新型微波功率测量仪器中,取得了很好的效果。     

基于特征集重构与多标签分类模型的谐波源定位方法

传统基于谐波状态估计的谐波源定位方法需要专门的同步相量量测装置,工程应用受到限制。为此,基于电能质量监测装置所采集的非同步量测数据,提出了基于特征集重构与多标签分类模型的谐波源定位方法。利用监测数据的充分统计量来挖掘量测时段的谐波信息,同时利用标签特定特征学习算法重构特征集,从而消除冗余特征以及无关特征对于谐波源定位精度的影响;提出基于邻接矩阵以及灵敏度分析的测点配置方法,结合电路网络拓扑信息实现测点的优化配置;提出基于改进极限学习机的谐波源定位方法,该方法以重构特征集为输入,建立多标签分类模型,实现谐波源定位。通过仿真与算例分析,验证了所提方法的可行性及有效性。     

水轮机自动数据采集与处理系统的精度问题

本文阐述水轮机自动数据采集与处理系统误差的特征和精度设计原则,结合性能参数实测中传感器静态特性曲线拟合方法的最佳选择和脉动特性测试精度的估算,系统地作了论述,还对电磁流量计精度一定时,提高测流精度的途径以及试验曲线总精度的确定方法作了介绍,从而便于对系统全面地进行精度控制.     

基于SC-DNN和多源数据融合的新能源电力系统状态估计方法

大规模新能源并网重塑了电力系统的控制运行特性,现有的电力系统状态估计方法面临新能源波动数据识别困难、估计精度低、估计速度慢等问题。为改善现有方法的不足,提出了一种基于残差连接(skip connection, SC)-深度神经网络(deep neural network, DNN)和多源数据融合的新能源电力系统状态估计方法。首先采用基于双向长短期神经网络(bidirectional long short-term memory, BILSTM)预测的改进插值法进行多源数据融合。然后利用联合时空交叉机制和BILSTM网络的数据辨识技术替代传统的量测量突变检测法,以便更好地处理新能源波动数据。最后根据原始量测数据集建立基于SC-DNN的状态估计模型,把残差模块的拟合优势和神经网络的速度优势结合起来,从而实现状态估计精度和速度的提高。基于IEEE39节点系统和新疆某地区实网的算例分析表明,相比于传统方法,所提方法能在更准确地分辨新源波动数据与不良数据的同时提高状态估计的精度和速度。     

基于广域测量的在线电压稳定监视

广域测量技术的发展和应用为电力系统电压稳定监视奠定了基础。对采用局部电压、电流变化估计电网侧等值阻抗进行了分析,指出利用SCADA/EMS测量数据不能准确估计电网侧等值阻抗的缺陷,提出基于节点电压和负荷电流相量变化的改进方法和电压稳定性指标。采用广域测量系统间接地避免潮流方程在临界点的不收敛,弥补传统方法在应用中存在的系统规模过大、非线性模型不准确、数据多且更新不及时、速度慢等缺陷。通过仿真计算表明该指标能够正确指示节点的电压稳定性,适于对节点在线静态电压稳定的监视,具有一定实用性。     

基于DSP和MCU的电能质量在线监测装置的设计

近年来,电能质量出现了日益恶化的趋势,因此有必要对其进行在线监测,以便采取措施改善、提高供电质量。鉴于此,本文设计了基于DSP和MCU的电能质量在线监测装置,充分发挥了DSP强大的信号处理和MCU完善的管理能力,实现了电网数据的采集、处理、存储、显示以及与上位机通信等功能。文中主要介绍了装置的基本构成、硬件和软件设计、电能质量分析算法以及所采取的抗干扰措施。实际运行表明,该监测装置具有稳定可靠、精度高、实时性好等优点,很好的满足了电能质量在线监测的实际需求。     

量测不同步与系统运行快变影响的状态估计精度的分析

量测不同步与负荷快变共同作用时,会导致数据采集与检测控制系统SCADA量测的数据质量变差,从而对状态估计的精度产生影响。为了改善上述情况下状态估计的精度,本文从实际电网出发运用“外推法”和相关系数法相结合的方法确定系统的量测不同步线路和不同步线路的问题端,然后用“平移法”与相关系数方法相结合的方法提高数据质量,使状态估计的精度得到提高。最后,通过省级的实际电力系统网络验证了方法的有效性。     

A new power system transient stability assessment method based on Type-2 fuzzy neural network estimation

Transient stability assessment (TSA) of large power systems by the conventional method is a time consuming task. For each disturbance many nonlinear equations should be solved that makes the problem too complex and will lead to delayed decisions in providing the necessary control signals for controlling the system. Nowadays new methods which are devise artificial intelligence techniques are frequently used for TSA problem instead of traditional methods. Unfortunately these methods are suffering from uncertainty in input measurements. Therefore, there is a necessity to develop a reliable and fast online TSA to analyze the stability status of power systems when exposed to credible disturbances. We propose a direct method based on Type-2 fuzzy neural network for TSA problem. The Type-2 fuzzy logic can properly handle the uncertainty which is exist in the measurement of power system parameters. On the other hand a multilayer perceptron (MLP) neural network (NN) has expert knowledge and learning capability. The proposed hybrid method combines both of these capabilities to achieve an accurate estimation of critical clearing time (CCT). The CCT is an index of TSA in power systems. The Type-2 fuzzy NN is trained by fast resilient back-propagation algorithm. Also, in order to the proposed approach become scalable in a large power system, a NN based sensitivity analysis method is employed to select more effective input data. Moreover, In order to verify the performance of the proposed Type-2 fuzzy NN based method, it has been compared with a MLP NN method. Both of the methods are applied to the IEEE standard New England 10-machine 39-bus test system. The simulation results show the effectiveness of the proposed method in compare to the frequently used MLP NN based method in terms of accuracy and computational cost of CCT estimation for sample fault scenarios.     

On the construction of power quality control systems

Some aspects of the construction of power quality control systems in power supply systems are discussed. The basic requirements for quality factors of electric power are determined, the conditions of joint parallel operation of energy consumers are considered, and analysis of traditional ways of solving the emerging problems is carried out. The advantages and disadvantages of power quality control systems related to the capabilities of new technologies with application of high-power semiconductor switching devices (GTO and IGCT thyristors, MOSFET and IGBT transistors) are considered. The application of computer technology in power quality control systems makes it possible to use rather complicated and effective algorithms of numerical processing of signals and significantly increase the accuracy of correction of disturbances in electric networks. The advantages and disadvantages of industrial plants constructed in the form of a controlled reactor with a dc magnetic bias and a reactor with a thyristor key are considered. Two types of devices to compensate for possible disadvantages are identified: devices using fully controlled keys and devices ones using artificial commutation and incompletely controlled power keys. A brief analysis of the known methods of electric power quality control is implemented. Recommendations for the establishment of power quality control systems are suggested in which the parameters of software depend on the solution algorithm of a posed problem, while the algorithm itself depends on the method of control and mathematical laws on which it is based. The method of control determines the structure of the power quality control system, while the software determines the basic operations of information processing, their quantity, the scheme of data processing, the volume data flows, etc.     

Harmonics estimation in emerging power system: Key issues and challenges

The world wide increasing applications of nonlinear loads, mostly consisting of power electronics devices, have made the power quality problems a vital issue than ever before. An efficient control and mitigation of power quality parameters are highly dependent on their accurate and timely detection. Harmonics are not only one of the prime culprits that deteriorate power quality of the supply system but it also affects accurate estimation and effective control of power quality problems. In this paper, several methods of power system harmonics estimation are critically reviewed and classified based on the type of analysis tool and applications. Various estimation techniques are discussed in brief; comparison of available approaches are examined and presented. The key issues and challenges in harmonics estimations are highlighted. A vast collection of papers, books and standards are listed in the reference list, which is useful to the researchers, engineers and policy-makers in the area of power quality.     

考虑大规模风电接入的快速抗差状态估计研究

提出了精细化抗差最小二乘状态估计方法,用于解决大规模风电接入对状态估计带来的残差污染问题。该方法一方面在权函数中引入量测类型基准值,用于区分不同类型量测坏数据,提高了抗差状态估计的坏数据检测能力。另一方面,利用状态估计量测预校验信息,对SCADA量测进行预处理,形成坏数据参考因子,消除参数误差引起的坏数据误判,从而提高大规模风电接入电网的状态估计计算精度。同时使用Givens变换并行算法提升软件计算速度,提高抗差状态估计数据断面的实时性,实现精细化的快速抗差状态估计,以适应风电的大规模接入电网给分析控制类在线应用带来的影响。最后对某地区电网进行测试验证,证明该方法能够有效识别风电场遥测坏数据,消除其造成的残差污染,提高了估计计算速度和精度。     

电力系统高精度频率估计的谱泄漏对消算法

提出基于谱泄漏对消技术的电力系统频率估计方法.该法通过将两段采样起点错开1/4个额定周期的采样信号序列的加窗傅里叶变换将基波的谱泄漏相消,同时也能显著减小其它奇次谐波的谱泄漏对频率测量的影响,从而极为有效地减小因采样不同步及信号畸变而引起的测量误差.由于电力系统频率成分主要为基波分量和小部分奇次谐波,因此该法能够显著地提高频率测量的精度.该法除了估计精度高以外,还具有时滞小和计算量小(只需对采样数据求加权和)等优点,适合于实时高精度频率测量.