基于NNAC算法的电能质量暂态扰动检测

针对当前电能质量检测分析的难点和重点问题,在分析了目前使用最多的方法小波变换优缺点的基础上,提出了基于神经网络自适应控制（NNAC）的电能质量暂态扰动检测算法。给出了电能质量暂态扰动检测的自适应控制结构,采用Hebb学习规则进行权值学习,并对电压暂降、电压瞬升、电压中断和暂态振荡等暂态扰动进行了仿真测试,结果表明所提算法可以很好地检测电网中的暂态扰动信号的类型,确定扰动发生的起始时刻和持续时间,且分析计算简单,速度快,计算所得数据量少,在电能质量扰动检测中更加具有实时性。     

基于db4提升小波的暂态电能质量的检测

暂态扰动是影响电能质量的重要因素之一,对电力用户和电力系统都会产生危害。针对暂态扰动信号具有非平稳性、突变性的特点,分析了db4提升小波变换的特性,提出了更新—预测—更新—预测—更新的结构,选择了合适的采样频率和分解层数,对暂态电能质量扰动信号进行检测与定位。在MATLAB仿真环境下,利用db4提升小波变换对电压暂降、暂升、中断以及暂态脉冲、振荡等几种暂态电能质量扰动信号进行检测与定位,仿真结果表明,该方法可以实现对扰动信号起止时刻更为准确的检测与定位,且计算速度快。     

基于小波包能量熵的电能质量扰动识别

提出了一种基于小波包能量熵的电能质量扰动识别方法。该方法对仿真的扰动电压信号进行4层小波包分解,提取小波包能量熵特征向量,利用主分量分析法提取电压信号的小波包特征向量并输入到概率神经网络(PNN)进行扰动识别,实现了扰动样本的最优压缩,简化了扰动分类中神经网络分类器的结构,提高了神经网络扰动识别的速度和精度。仿真结果表明,该方法具有良好的扰动识别能力。     

叠加暂态电能质量扰动的分形熵分析

暂态电能质量会给敏感用户带来重大损失,因此,在最普遍情况下识别叠加的暂态电能质量扰动非常重要。从图形模式识别的角度出发,基于分形理论对数据进行分段,并构建含扰动波形的能量熵,在此基础上通过最大熵方法辨识特征最为显著的扰动。随后在波形中去掉该扰动,在剩下的波形中辨识新的暂态扰动,由此可逐个辨识叠加波形中所有的暂态电能质量扰动。仿真实验表明,该算法具有较好的适应性及稳健性,可以在噪声环境中识别叠加的小幅度暂态电能质量扰动,识别率也较高。     

基于MAP估计双树复小波的电能质量扰动信号去噪方法

针对电能质量信号的去噪,提出了一种基于MAP估计的双树复小波电能质量扰动信号的去噪方法。首先对带噪信号进行相关性预处理,然后通过MAP方法对双树复小波分解不同层次的细节系数估计噪声方差和信号方差,并计算各层阀值从而得到去噪方案,针对带噪的电压跌落等扰动信号进行仿真,并与传统实小波去噪进行了信噪比和突变点信息保留能力的比较。仿真结果表明,所提算法速度快,去噪效果理想,且易于实现,实用性强,有良好的发展前景。     

一种基于小波变换的电能质量特征量提取及分类的方法

提出了一种利用小波变换实现动态电能质量特征量扰动(电压上升、电压凹陷、电压中断、电压瞬变和电压缺口等)的提取以及分类方法. 利用小波变换的奇异性检测原理提取动态电能质量信号中的突变, 并进行定位, 确定信号变化的时间,并用电压均值法确定扰动的类别, 并通过仿真表明方法的有效性. 最后讨论了本方法在DSP上的实现.     

基于自抗扰控制技术的发电机励磁控制系统

采用合理的发电机励磁控制方案对改善电力系统小扰动及大扰动稳定性有着重要的作用。介绍了自抗扰控制器的原理;分析了传统的PID励磁控制器无法实用的原因;针对其固有的缺陷,提出了一种基于自抗扰控制技术的新型励磁控制策略,并分析了其动态性能较PID控制器优异的原因。仿真结果表明,所提出的励磁控制策略能快速抑制发电机端电压的大幅振荡,有效地改善系统的动态品质,提高系统的稳定水平。     

基于组合特征和BTSVM的电能质量扰动识别

为了克服单一特征不能完全表征各种暂态扰动信号特征的不足,提出了一种基于组合特征和二叉树结构支持向量机相结合的电能质量多分类方案。利用小波包变换对扰动信号进行分解,提取特定频带下信号的能量,利用S变换获得扰动信号的模矩阵,从中提取出特征信息,然后将多频带信号的能量和对应的S变换特征信息组合得到组合特征。对依据聚类思想设计出的二叉树结构支持向量机分类器进行了训练和测试。仿真结果表明,该方法具有较好的准确性和识别速度,能够有效识别常见扰动信号,平均识别率提高了6%以上,测试总用时缩短0.06秒,训练时间减小1.8秒。     

电能质量干扰发生装置的研制

电能质量干扰发生器常用于检验电力设备受电能扰动时的性能,提出了一种基于波形信号发生器加线性功率放大器的电能质量干扰发生器装置的设计方案.以可编程逻辑器件EPM7128、双口RAM和数模转换器为基础,采用直接数字合成技术设计了正弦信号产生电路.利用SM5964单片机对数模转换器的参考电压进行控制来改变波形输出幅度,实现小信号的聚升、跌落、中断和凹陷等功能.通过高压高速集成功率运放PA89A对小信号进行幅度放大,采用高压高频三极管并联技术设计了扩流电路.使用Multisim软件对功率放大电路的参数进行仿真和优化,仿真实验结果表明,该电能干扰发生装置的输出电压幅值在0 ～264 V范围内任意可调,负载电流达到2A,并能实现电压跌落、聚升、中断和凹陷等功能.     

基于VSG微网储能变流器无缝切换控制策略研究

针对微网储能变流器采用状态跟随控制器进行切换的过程中存在微小电压、电流扰动等问题,本文提出了一种基于线性自抗扰控制器(LADRC)的无缝切换控制策略。在采用P/Q控制和VSG控制相互切换方法的基础上,将电流内环控制器改成线性自抗扰控制器。通过设计一个状态观测器LESO实时在线观测估计输出变量直轴、交轴电流id、iq以及系统的各种扰动;将扰动估计值补偿给P/Q控制器和VSG控制器切换过程中的突变量,从而抑制切换过程中的电压、电流波动。最后,建立基于LADRC的储能变流器无缝切换仿真模型。仿真结果验证了该方法的可行性。     

Hybrid causal analysis combining a nonparametric multiplicative regression causality estimator with process connectivity information

Industrial processes are often subjected to abnormal events such as faults or external disturbances which can easily propagate via the process units. Establishing causal dependencies among process measurements has a key role in fault diagnosis due to its ability to identify the root cause of a fault and its propagation path. This paper proposes a hybrid nonlinear causal analysis based on nonparametric multiplicative regression (NPMR) for identifying the propagation of an oscillatory disturbance via control loops. The NPMR causality estimator addresses most of the limitations of the linear model-based methods and it can be applied to both bivariate and multivariate estimations without any modifications to the method parameters. Moreover, the NPMR-based estimations can be used to pinpoint the root cause of a fault. The process connectivity information is automatically integrated into the causal analysis using a specialized search algorithm. Thereby, it enables to efficiently tackle industrial systems with a high level of connectivity and enhance the quality of the results. The proposed approach is successfully demonstrated on an industrial board machine exhibiting oscillations in its drying section due to valve stiction and. The NPMR-based estimator produced highly accurate results with relatively low computational effort compared with the linear Granger causality and other nonlinear causality estimators.     

非负矩阵分解方法识别电能质量扰动信号

利用相空间重构及非负矩阵分解(NMF)相结合的方法,对电压暂降、电压暂升、电压尖峰、电压中断、暂态谐波及暂态振荡6类电能扰动信号进行分类识别研究。利用相空间重构法构造扰动信号轨迹,并将其转化为图像。从图像处理的角度出发,利用NMF在人脸、指纹图像识别应用中的基本原理,对不同的相空间重构轨迹图进行特征提取,分类识别其所对应的电能质量扰动信号。该方法可避免由于扰动信号的复杂性而难以获得扰动信号稳定特征提取的困难,具有训练时间短、所需训练样本少、识别过程可视化便于分析等特点。仿真实验结果表明其能够较好地识别电能质量扰动,是提供了扰动信号检测与分类的算法。     

单元机组的非线性自适应反演综合控制

针对同时考虑参数不确定性和外界干扰的单元机组整体模型(锅炉–汽轮机–发电机模型),提出了一种基于自适应反演法和协调无源性理论的非线性综合控制策略.首先,针对预处理后的被控对象各子系统,采用反演法得到汽门控制输入,然后以高压侧电压为控制目标对励磁部分进行优化设计,最后逐步递推设计燃料控制器.在控制律设计过程中,为克服模型参数的不确定性,避免可能出现的参数漂移,通过引入充分光滑投影算子设计了参数自适应律.同时,借助在控制律中添加阻尼项,以有效抑制外界干扰对控制品质的影响.仿真分析和变负荷实验的结果表明,本文所设计的综合控制策略同时优化了单元机组的有功和无功响应特性,使其具有良好的负荷跟踪性能、抗干扰能力和对模型不确定参数的鲁棒性.     

一种改进的光伏阵列最大功率点跟踪方法

针对光伏阵列最大功率点跟踪常用的控制方法即恒压法、扰动观察法、增加电导法存在的问题,提出了一种将恒压法和扰动观察法相结合的快速最大功率点跟踪控制方法。该方法采用恒压法获取光伏阵列最大功率点处的近似电压,然后根据光伏阵列的输出电压与该近似电压的差值确定扰动步长:如果差值较大,则表明光伏阵列工作点离最大功率点比较远,应采用较大的步长;如果差值较小,应采用较小的步长,从而减小功率振荡。仿真和实验结果表明,该方法不仅可快速跟踪光伏阵列的最大功率点,而且可有效减小功率振荡。     

Classification of power system disturbances using support vector machines

This paper presents an effective method based on support vector machines (SVM) for identification of power system disturbances. Because of its advantages in signal processing applications, the wavelet transform (WT) is used to extract the distinctive features of the voltage signals. After the wavelet decomposition, the characteristic features of each disturbance waveforms are obtained. The wavelet energy criterion is also applied to wavelet detail coefficients to reduce the sizes of data set. After feature extraction stage SVM is used to classify the power system disturbance waveforms and the performance of SVM is compared with the artificial neural networks (ANN).     

Micro-genetic algorithms for detecting and classifying electric power disturbances

The power quality analysis represents an important aspect in the overall society welfare. The analysis of power disturbances in electrical systems is typically performed in two steps: disturbance detection and disturbance classification. Disturbance detection is usually made through space transform techniques, and their classification is usually performed through artificial intelligence methods. The problem with those approaches is the adequate selection of parameters for these techniques. Due to the advantages of a variant scheme known as the micro-genetic algorithms, in this investigation, a new methodology to directly detect and classify electrical disturbances in one step is developed. The proposed approach is validated through synthetic signals and experimental test on real data, and the obtained results are compared with the particle swarm optimization method in order to show the effectiveness of this methodology.

     

Unknown input modeling and robust fault diagnosis using black box observers

A key issue that needs to be addressed while performing fault diagnosis using black box models is that of robustness against abrupt changes in unknown inputs. A fundamental difficulty with the robust FDI design approaches available in the literature is that they require some a priori knowledge of the model for unmeasured disturbances or modeling uncertainty. In this work, we propose a novel approach for modeling abrupt changes in unmeasured disturbances when innovation form of state space model (i.e. black box observer) is used for fault diagnosis. A disturbance coupling matrix is developed using singular value decomposition of the extended observability matrix and further used to formulate a robust fault diagnosis scheme based on generalized likelihood ratio test. The proposed modeling approach does not require any a priori knowledge of how these faults affect the system dynamics. To isolate sensor and actuator biases from step jumps in unmeasured disturbances, a statistically rigorous method is developed for distinguishing between faults modeled using different number of parameters. Simulation studies on a heavy oil fractionator example show that the proposed FDI methodology based on identified models can be used to effectively distinguish between sensor biases, actuator biases and other soft faults caused by changes in unmeasured disturbance variables. The fault tolerant control scheme, which makes use of the proposed robust FDI methodology, gives significantly better control performance than conventional controllers when soft faults occur. The experimental evaluation of the proposed FDI methodology on a laboratory scale stirred tank temperature control set-up corroborates these conclusions.     

Hybrid Renewable Energy Source Combined Dynamic Voltage Restorer for Power Quality Improvement

In this paper, the hybrid photovoltaic-thermoelectric generator (PV-TEG) combined dynamic voltage restorer (DVR) system is proposed for the power quality disturbances compensation in a single-phase distribution system. The stable and precise level of input voltage is essential for the smooth and trouble-free operation of the electrically sensitive loads which are connected at the utility side to avoid system malfunctions. In this context, the hybrid PV-TEG energy module combined DVR system is proposed in this paper. With the support of the hybrid energy module, the DVR will perform the power quality disturbances compensation effectively with needed voltage and /or power. In the proposed system, the PV and TEG energy sources are connected electrically in series to produce adequate voltage for the DVR operation and the fractional factor-based variable incremental conduction (FFVINC) maximum power point tracking (MPPT) control algorithm is employed to extract the possible maximum power from the PV array. The intelligent fuzzy logic controller (FLC) is chosen for implementing the MPPT control algorithm. The half-bridge voltage source inverter (VSI) circuit and in-phase voltage compensation technique are used in the DVR for better power quality disturbances compensation. The performance and usefulness of the proposed DVR system are investigated by an extensive simulation study with four different modes of operation, the study results are confirmed that the proposed system promptly identifies the power quality disturbances for compensation. Moreover, the investigation proved that the combined PV and TEG energy module can provide better energy efficiency in converting solar irradiation into electricity.     

Microprocessor and PI controller based three phase CHBMLI based DSTATCOM for THD mitigation using hybrid control techniques

Nowadays, in power electronic devices the power quality associated problems are more preoccupied. Different methods of power devices are needed for compensating PQ disturbances. For increasing the PQ one of the power electronic devices used is Distributed STATCOM. The performance of DSTATCOM must be improved by reducing the PQ disturbances. For increasing the behaviour of DSTATCOM and PQ compensation a hybrid controller is developed in this paper. The Radial Basis Function Neural Network method is utilized by deduce the source current for compensation of DSTATCOM and decreasing PQ issues. By using DSTATCOM the harmonic disturbances are decreased. The developed hybrid controller method is utilized to make constant DC voltage in the DC link capacitor of the developed MLI. Consequently, developed method uses Harmonic analysis for estimating the THD values. The proposed technique is developed using matlab simulation. The new developed technique is appraised and differentiated with PI, fuzzy logic, microprocessor and ANN controllers.     

风电并网对系统稳定性及紧急控制策略影响的仿真分析

在DIgSILENT软件中建立异步风电机组和双馈感应风电机组模型,以新疆某地区实际电网为例,分析比较了两种不同风机并网对电力系统电压和频率稳定的影响,同时,研究了两种风机并网前后对电力系统紧急控制策略的影响。仿真结果表明:当电网发生扰动时,双馈感应风机频率响应不明显;但在电网扰动影响电压稳定时,双馈感应电机能够提高一定的电压稳定性;当电网发生大扰动,采取紧急控制策略后电网的稳定性不如电网未接入风电时电网采取紧急控制策略后的稳定性。