A Review on Intelligent Detection and Classification of Power Quality Disturbances:Trends,Methodologies,and Prospects

With increasing global concerns about clean energy in smart grids,the detection of power quality disturbances(PQDs)caused by energy instability is becoming more and more prominent.It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous,which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids.In order to ensure safe and reliable equipment implementation,appropriate PQDdetection technologiesmust be adopted to avoid such adverse effects.This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field,where specific scenarios and events for which each technique is applicable are also clearly presented.Finally,comments on the future evolution of PQD detection techniques are given.Unlike the published review articles,this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

A novel stabilization approach for small signal disturbance of power system with time-varying delay

Small signal instability may cause severe accidents for power system if it can not be dealt correctly and timely. How to maintain power system stable under small signal disturbance is a big challenge for power system operators and dispatchers. Time delay existing in signal transmission process makes the problem more complex. Conventional eigenvalue analysis method neglects time delay influence and can not precisely describe power system dynamic behaviors. In this work, a modified small signal stability model considering time varying delay influence was constructed and a new time delay controller was proposed to stabilize power system under disturbance. By Lyapunov-Krasovskii function, the control law in the form of nonlinear matrix inequality(NLMI) was derived. Considering synthesis method limitation for time delay controller at present, both parameter adjustment method by using linear matrix inequality(LMI) solver and iteration searching method by solving nonlinear minimization problem were suggested to design the controller. Simulation tests were carried out on synchronous-machine infinite-bus power system. Satisfactory test results verify the correctness of the proposed model and the feasibility of the stabilization approach.

Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms

The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification.

Classification of power quality combined disturbances based on phase space reconstruction and support vector machines

Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.

Flux Feedback Power Amplifiers Based on a Disturbance Observer

磁通反馈式功率放大器中存在的噪声、参数摄动以及其它非线性特征会影响磁悬浮效果。为了研究该问题,建立了磁通反馈式功率放大器及其负载电磁铁的仿真模型,其中引入了气隙变化对磁通生成模型的影响。针对单纯比例积分微分（proportion-integral-differential,PID）控制应用在磁通反馈式功率放大器中会产生磁通稳态输出抖动的问题,设计了一个新型干扰观测器（disturbance observer,DOB）。新型DOB采用多路传感信号综合观测的方式减小反馈信号处的噪声干扰对观测值的影响,并获得了比常规DOB更加稳定的系统等效效果。仿真结果表明,结合新型DOB结构的PID反馈控制方法能够很好的抑制因模型参数摄动、模型非线性特征和噪声而造成的稳态磁通输出抖动,其控制效果优于单独PID控制方法。

A Quick Classification Method of the Power Quality Disturbances

This paper introduces a quick classification method of the power quality disturbances. Based on analyzing the characteristics of different electrical disturbance signals in time domain, four distinctive features are extracted from electrical signals for classifying different power quality disturbances and then an automatic classifier is proposed. Using the proposed classification method,a PQ monitor of the classifying power quality disturbances is developed based on the TMS320F2812DSP micro-processor. Semi-physical simulation, lab experiment and field measurement results have verified that this proposed method can classify single or complex disturbance signals effectively.

Recognition of Hybrid PQ Disturbances Based on Multi-Resolution S-Transform and Decision Tree

Aiming at the problems of multiple types of power quality composite disturbances,strong feature correlation and high recognition error rate,a method of power quality composite disturbances identification based on multiresolution S-transform and decision tree was proposed.Firstly,according to IEEE standard,the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given,and the disturbance waveform samples are generated in batches.Then,in order to improve the recognition accuracy,the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis.On this basis,five disturbance time-frequency domain features are extracted,which quantitatively reflect the characteristics of the analyzed power quality disturbance signal,which is less than the traditional method based on S-transform.Finally,three classifiers such as K-nearest neighbor,support vector machine and decision tree algorithm are used to effectively complete the identification of power quality composite disturbances.Simulation results showthat the classification accuracy of decision tree algorithmis higher than that of K-nearest neighbor and support vector machine.Finally,the proposed method is compared with other commonly used recognition algorithms.Experimental results show that the proposedmethod is effective in terms of detection accuracy,especially for combined PQ interference.

Power Quality Improvement Using ANN Controller For Hybrid Power Distribution Systems

In this work,an Artificial Neural Network(ANN)based technique is suggested for classifying the faults which occur in hybrid power distribution systems.Power,which is generated by the solar and wind energy-based hybrid system,is given to the grid at the Point of Common Coupling(PCC).A boost converter along with perturb and observe(P&O)algorithm is utilized in this system to obtain a constant link voltage.In contrast,the link voltage of the wind energy conversion system(WECS)is retained with the assistance of a Proportional Integral(PI)controller.The grid synchronization is tainted with the assis-tance of the d-q theory.For the analysis of faults like islanding,line-ground,and line-line fault,the ANN is utilized.The voltage signal is observed at the PCC,and the Discrete Wavelet Transform(DWT)is employed to obtain different features.Based on the collected features,the ANN classifies the faults in an effi-cient manner.The simulation is done in MATLAB and the results are also validated through the hardware implementation.Detailed fault analysis is carried out and the results are compared with the existing techniques.Finally,the Total harmonic distortion(THD)is lessened by 4.3%by using the proposed methodology.

基于佳点集-t分布变异的麻雀算法VSG控制策略研究

为进一步优化虚拟同步发电机在给定有功扰动或电网频率突变扰动时存在的问题,提出了一种基于佳点集-t分布变异的麻雀算法VSG控制策略,该策略采用佳点集对麻雀种群进行初始化,引入正态分布修改发现者位置更新公式,并在麻雀个体位置更新后加入自适应t分布变异策略,利用测试函数验证了ISSA算法具有良好寻优能力。在VSG控制单元中引入ISSA算法,对VSG系统参数进行优化,获得一组符合需求的最优解。利用Simulink对比仿真常规VSG控制策略、SSA优化下的VSG和ISSA优化下的VSG控制策略,验证了当系统出现扰动时,该控制策略对于频率和有功功率振荡均有较好的抑制效果,且相对于另外两种控制策略,在响应速度、超调量、稳定性等方面的表现更优。

基于特征图像组合与改进ResNet-18的电能质量扰动识别方法

针对传统电能质量扰动(power quality disturbance,PQD)识别体系中单一图像特征信息受限与算法识别能力不足等问题,依据特征融合的思想,提出一种基于特征图像组合与改进ResNet-18的PQD识别方法。首先,对PQD信号进行变分模态分解(variational mode decomposition,VMD)得到一系列固有模态函数(intrinsic mode functions,IMFs)与残差分量;其次,将IMFs、残差分量、原始扰动信号与Subtract分量纵向拼接成分量矩阵,利用信号-图像转化方法生成特征分量彩色图;再次,对原始扰动信号进行连续小波变换(continuous wavelet transform,CWT)生成小波时-频图;最后,将特征分量彩色图与小波时-频图组合输入改进的六通道ResNet-18中训练学习并完成扰动识别。通过仿真对PQD识别方法进行分析并将其与目前常用识别体系进行比较。结果表明,所提方法具有较好的抗噪性能并且能够更好地提取PQD特征信息,达到更高的识别准确率。

基于深度学习的复合电能质量扰动识别方法

精准的电能质量扰动识别是对电能质量扰动事件发生后需要解决的主要问题之一,这对划分责任和加快电力市场化进程均具有重要意义,而海量的电能质量监测数据则为电能质量扰动识别提供了条件与机遇。不同的电能质量扰动类型,其电气特征上也存在区别,故可利用不同电能质量扰动波形之间的差异来区分电能质量扰动类型。结合深度学习理论,建立一种基于双向独立循环神经网络的复合电能质量扰动识别方法,通过提取电能质量扰动信号的本质特征量,建立输入序列与输出序列之间的内在对应关系,克服了分析结果对物理特征量的依赖性,提升了电能质量扰动识别准确率。实验结果表明,所提方法可以有效应对复合电能质量扰动的多样性问题,可以直接从原始的底层数据中自主学习复合电能质量扰动信号中所隐藏的本质特征量,识别准确率高。

基于卷积神经网络与Transformer的电能质量扰动分类方法

复杂电能质量扰动(power quality disturbances, PQD)的智能分类对于智能电网发展具有重要意义。扰动特征的提取与定位、模式识别与分类是电能质量扰动分类方法研究的难点。采用深度学习算法,将具有关注全局信息的Transformer与善于提取局部特征的卷积神经网络相融合,提出一种基于卷积神经网络(convolutional neural network, CNN)与Transformer的电能质量扰动分类方法,即CTranCBA。这种双深度学习模型分类方法主要是通过一维卷积神经网络提取电能质量扰动信号特征,利用Transformer自注意力机制引导模型关注序列中不同位置间的依赖关系,实现对扰动信号局部特征与全局特征的互补,克服了因感受野的限制而带来的识别不清、分类不准等问题。使用23种不同电能质量扰动信号,将CTranCBA与Deep-CNN、CNN-LSTM、CNN-CBAM方法进行比较。结果表明:该方法在分类准确率和抗噪性方面表现优异,可为电能质量扰动智能分类提供一种新的方法。

双绕组感应发电机交直流集成发电系统的自抗扰控制策略

针对双绕组感应发电机交直流集成发电系统,为实现简单易调、性能优越、负载适应力强的发电控制,该文提出一种基于线性自抗扰控制器的交直流电压控制策略,通过交流电压和直流电压与控制绕组和功率绕组的传递函数模型,构建交直流电压的线性扩张状态观测器,同时对交流电压有效值、直流电压和电压扰动进行观测,并利用电压和扰动观测值设计线性误差反馈控制律,对交直流电压进行控制。最后,通过仿真和实验验证该控制策略提高电压扰动抑制能力和负载适应性,同时各模块参数可独立设计、控制器结构简单,避免需要较多传感器及降低调参难度。

经验小波变换和改进S变换结合的电能质量检测与识别方法

为分析不确定干扰因素影响下的实际电力网络电能质量问题,提出一种经验小波变换(EWT)和改进S变换相结合的电能质量检测与识别方法。该方法一方面利用EWT联合归一化直接正交(NDQ)算法和奇异值分解(SVD)算法准确提取调幅-调频分量的频率、幅值和时间参数,另一方面考虑到EWT算法在高噪声环境下瞬时幅值波动的问题,引入改进S变换提取高噪声干扰下的电能质量扰动时频信息,最后,基于EWT和改进S变换提取的扰动特征向量,利用基于改进粒子群优化算法(IPSO)优化支持向量机(SVM)的电能质量扰动识别分类器实现扰动类型的精确识别。仿真和实验表明所提方法在复合扰动识别分类时平均识别准确率为93.23%,且能够准确识别4种实测扰动信号。

基于卡尔曼滤波的改进ADRC风电制动器控制策略研究

为了提高风电制动器的抗干扰能力和控制精度,在传统三闭环系统的基础上,提出改进自抗扰控制并融合卡尔曼滤波算法.在扩张状态观测器中引入速度跟踪情况,将传统的“大带宽、小误差”改进为“小带宽、小误差”,提高观测器的观测效率.在误差反馈控制率中引入新型指数趋近律的滑模控制,增强控制效果.在电流环中引入卡尔曼滤波算法,降低电流噪声.通过MATLAB/Simulink建立风电制动系统的数学模型,与传统自抗扰控制策略和PID控制策略进行对比分析.结果表明:改进后的控制策略提高了响应速度,降低了启动转矩和电流噪声;制动器在不同工况下均展现出来良好的控制效果和抗干扰能力,提升了系统的鲁棒性和动态性能.

大扰动下缺额功率分配及频率分布特性研究

特高压交直流输电工程不断投运,电网功率扰动事件频发。同时越来越多的新能源在电网中取代部分传统机组,导致电力系统的惯量水平下降,对频率支撑能力下降。为此,研究了电网受扰时不平衡功率的分配特性、频率压降的分布特性以及在新能源机组附近产生的连锁反应。根据频率分布的特点提出了区域惯量的概念,并进行仿真分析。结果表明,不平衡功率分配大小与线路阻抗有关,且频率变化率具有分布特性频率响应的分布特性有利于后期新能源并网惯量调频措施的研究。

基于等价输入干扰的分布式光伏电源出力控制策略

分布式电源能够改善配电网潮流分布,使得电力系统具有更高的能源利用率以及灵活性,然而分布式电源的不合理出力则难以充分发挥自身优势,并且死区效应、电网电压畸变及系统参数不确定性等不确定性扰动也会对电力系统的可靠性构成严重威胁。针对此问题,提出了基于改进的等价输入干扰方法(improvedequivalentinputdisturbance,IEID)的分布式电源出力控制方法。其中上层优化结构以配电网网损最小为目标,建立配电网优化降损模型并求解得到分布式电源出力值;下层控制结构基于所提IEID设计分布式电源控制策略,把模型耦合项、死区效应、网压畸变及系统参数不确定性等干扰对系统输出的影响等价到输入通道上,并对其进行估计和补偿,能够有效地抑制未知扰动并高精度跟踪上层输出的出力值,极大地改善并网电能质量。以某21节点真实居民配电网为算例,仿真结果表明所提方法具有较好的扰动抑制能力,能够自主平稳出力,降低配电网网损。

融合随机森林和神经网络的电能质量分析算法

提出了一种融合随机森林(RF)和神经网络(NN)的电能质量分析算法.首先利用RF对电能质量信号进行特征提取和降维,然后利用NN对提取的特征进行分类和识别,最后通过实验验证了该算法的有效性,并与其他常用的电能质量分析方法进行了比较.实验结果表明,该算法具有较高的准确率、召回率和F1值,以及较快的运行速度和较低的计算复杂度.

基于SA-PSO算法优化CNN的电能质量扰动分类模型

针对传统电能质量扰动分类模型中扰动特征复杂、识别步骤繁琐的问题,提出了一种通过模拟退火(SA)算法与粒子群优化(PSO)算法相结合来优化卷积神经网络(CNN)的电能质量扰动分类模型。将CNN卷积层中的二维卷积核替换成一维卷积核;采用SA算法对PSO算法进行改进,规避PSO算法陷入局部最优的困境;采用改进后的PSO算法对CNN进行参数寻优;利用优化CNN提取和筛选合适的特征,根据这些特征利用分类器得到最终分类结果。通过算例分析得出,使用基于SA-PSO算法优化的CNN的电能质量扰动分类模型能精确地识别出电能质量扰动信号。