Characterization of power quality disturbances using hybrid technique of linear Kalman filter and fuzzy-expert system

This paper presents a hybrid technique for characterizing power quality (PQ) disturbances. The hybrid technique is based on Kalman filter for extracting three parameters (amplitude, slope of amplitude, harmonic indication) from the captured distorted waveform. Discrete wavelet transform (DWT) is used to help Kalman filter to give a good performance; the captured distorted waveform is passed through the DWT to determine the noise inside it and the covariance of this noise is fed together with the captured voltage waveform to the Kalman filter. The three parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to characterize the PQ events in the captured waveform. This hybrid technique can classify two simultaneous PQ events such as sag and harmonic or swell and harmonic. Several simulation and experimental data are used to validate the proposed technique. The results depict that the proposed technique has the ability to accurately identify and characterize PQ disturbances.     

Kalman filter for identification of power system fuzzy harmonic components

The ability to estimate the harmonic components in a power system is necessary for delivering a high quality power to the end user. This paper proposes a new approach for identifying harmonic components in a power system based on treating the non-fundamental sinusoid voltage or current waveform as a fuzzy noise having a linear model. The parameters of this model are assumed to be fuzzy numbers with a membership function that has central and spread values. Kalman filter is used for identifying the center and spread of each coefficient. It is assumed that the distortion of the sinusoid waveform is due to noise and/or undesired sinusoidal components with different frequencies. Kalman filter filters the noise, the undesired components will be estimated as a spread in the membership functions of the coefficients. Numerical examples are presented to illustrate the effectiveness of this technique.     

Application of minimal radial basis function neural network todistance protection

The paper presents a new approach for the protection of power transmission lines using a minimal radial basis function neural network (MRBFNN). This type of RBF neural network uses a sequential learning procedure to determine the optimum number of neurons in the hidden layer without resorting to trial and error. The input data to this network comprises fundamental peak values of relaying point voltage and current signals, the zero-sequence component of current and system operating frequency. These input variables are obtained by a Kalman filtering approach. Further, the parameters of the network are adjusted using a variant of extended Kalman filter known as locally iterated Kalman filter to produce better accuracy in the output for harmonics, DC offset and noise in the input data. The number of training patterns and the training time are drastically reduced and significant accuracy is achieved in different types of fault classification and location in transmission lines using computer simulated tests     

Adaptive estimation of power system frequency deviation and itsrate of change for calculating sudden power system overloads

A novel Kalman filtering-based technique is presented for estimating power system frequency deviation and its average rate of change during emergency operating conditions that may require load shedding. This method obtains the optimal estimate of the power system frequency deviation from noisy voltage samples and the best estimate of the mean system frequency deviation and its rate of change while accounting for low-frequency synchronizing oscillations which occur during large disturbances. The proposed technique is a two-stage algorithm which uses an adaptive extended Kalman filter in series with an adaptive linear Kalman filter. The extended Kalman filter calculates the frequency deviation, magnitude, and phase angle of the voltage phasor, which may change during the time period covered by the estimation window. Both the measurement noise variance and the system noise covariance associated with the voltage samples are calculated online. The instantaneous frequency deviation is used as the input to a linear Kalman filter, which models the frequency deviation as a random walk plus a random ramp process. The estimated average rate of frequency decay is represented by the slope of the random ramp. Results for both single and multiple measurements are reported     

A new approach to identification of transient power quality problems using linear combiners

In this paper, we present a new approach to identify transient power quality disturbances using linear combiners and a fuzzy decision support system. The key idea underlying the approach is to obtain the amplitude and the slope of the peak fundamental component of a voltage waveform using adaptive linear combiners, along with nonlinear least mean squares (LMS) algorithm. Fuzzy logic is then used to identify the class to which the waveform belongs by a set of heuristic rules and an uncertainty index. Detailed digital simulation results involving various types of transient power quality disturbances are presented to prove the ability of the new approach in classifying these disturbances.     

Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm

In this paper, a new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm. In the proposed method, suitable features of the waveform of the PQ disturbance are first extracted. These features are extracted from parameters derived from the Fourier and wavelet transforms of the signal. Then, the proposed fuzzy system classifies the type of PQ disturbances based on these features. The PSO algorithm is used to accurately determine the membership function parameters for the fuzzy systems. To test the proposed approach, the waveforms of the PQ disturbances were assumed to be in the sampled form. The impulse, interruption, swell, sag, notch, transient, harmonic, and flicker are considered as single disturbances for the voltage signal. In addition, eight possible combinations of single disturbances are considered as the PQ combined types. The capability of the proposed approach to identify these PQ disturbances is also investigated, when white Gaussian noise, with various signal to noise ratio (SNR) values, is added to the waveforms. The simulation results show that the average rate of correct identification is about 96% for different single and combined PQ disturbances under noisy conditions.     

Classification of power system disturbances using a fuzzy expertsystem and a Fourier linear combiner

This paper presents a hybrid scheme using a Fourier linear combiner and a fuzzy expert system for the classification of transient disturbance waveforms in a power system. The captured voltage or current waveforms are passed through a Fourier linear combiner block to provide normalized peak amplitude and phase at every sampling instant. The normalized peak amplitude and computed slope of the waveforms are then passed on to a diagnostic module that computes the truth value of the signal combination and determines the class to which the waveform belongs. Several numerical tests have been conducted using EMTP programs to validate the disturbance waveform classification with the help of the new hybrid approach which is much simpler than the recently postulated ANN or wavelet based approaches     

Frequency estimation of distorted power system signals usingextended complex Kalman filter

The paper proposes an extended complex Kalman filter and employs it for the estimation of power system frequency in the presence of random noise and distortions. From the discrete values of the 3-phase voltage signals of a power system, a complex voltage vector is formed using the well known αβ-transform. A nonlinear state space formulation is then obtained for this complex signal and an extended Kalman filtering approach is used to compute the true state of the model iteratively with significant noise and harmonic distortions. As the frequency is modeled as a state, the estimation of the state vector yields the unknown power system frequency. Several computer simulations test results are presented in the paper to highlight the usefulness of this approach in estimating near nominal and off-nominal power system frequencies     

A Robust Extended Complex Kalman Filter and Sliding-mode Control Based Shunt Active Power Filter

Abstract—This article presents the design of a new shunt active power filter that employs a modified robust extended complex Kalman filter approach with an exponential robust term embedded for reference current estimation together with a current controller based on the sliding-mode control concept. The robust extended complex Kalman filter exploits a new weighted exponential function to handle these grid perturbations to estimate the reference signal in shunt active power filter system. The current controller in the proposed shunt active power filter has been designed using a sliding-mode control strategy because of its ability to handle parameter uncertainties and ease in implementation. To test the effectiveness of the proposed shunt active power filter, extensive simulations were performed using MATLAB/Simulink (The MathWorks, Natick, Massachusetts, USA), and real-time studies were made using OPAL-RT (Montreal, Quebec, Canada). Results obtained from the above studies using the proposed shunt active power filter together with the different variants of Kalman filter (Kalman filter, extended Kalman filter, extended complex Kalman filter) are analyzed, and it is observed that the proposed robust extended complex Kalman filter-sliding-mode control based shunt active power filter provides accurate and improved harmonics mitigation and reactive power compensation.     

扩展卡尔曼滤波在动态负荷参数辨识中应用

采用扩展卡尔曼滤波算法建立由动态负荷和静态负荷组成的综合负荷数学模型,并列出了其转子运动方程、状态方程和输出方程,其中动态负荷由等值的异步电机表示,静态负荷由恒定导纳并联组成。通过动模试验,取得给定负荷在系统扰动时的电压、电流数据。根据所建立数学模型的输入、输出值,用扩展卡尔曼滤波算法辨识其中的待定参数。参数初值设置为真值的2～7倍,辨识结果误差为2%～3%。分析结果表明,扩展卡尔曼滤波可在短时间内收敛,能正确地辨识出系统参数,且稳定性好。结论表明扩展卡尔曼滤波可以用于电力系统参数辨识,为电力系统状态估计、负荷建模提供了有效方法。     

卡尔曼滤波理论在电力系统中的应用综述

现代电力系统中,由于可再生能源输出功率、负荷变化以及其他随机过程的存在,使得系统状态参数中往往混杂着噪声,因此有必要采取适当的方法,从随机干扰的观测信号中提取有效的系统状态参数。首先对卡尔曼滤波基本理论进行了介绍,给出了卡尔曼滤波的基本过程。然后主要综述卡尔曼滤波及其扩展形式在电力系统短期负荷预测、动态状态估计、电能质量分析、继电保护、风电场风速预测、电机状态和参数估计等方面的应用。最后给出了卡尔曼滤波在电力系统中应用的相关结论及其未来发展趋势。     

一种新的小电流接地选线算法

在分析以往小电流接地选线各种算法的基础上,提出了一种基于数学形态学及小波变换综合的选线算法,该算法利用数学形态学滤波器对采集到的电压电流信号进行滤波,而后利用小波变换的奇异性理论和模极大值理论来构成接地选线算法,本算法很好地体现了形态学滤波器的噪声抑制能力和小波分解的奇异点检测能力。Matlab仿真结果表明,该算法不受中性点接地方式和线路参数的影响,且具有较强的承受过渡电阻的能力。     

基于分段改进S变换的复合电能质量扰动识别

针对存在多种单一电能质量扰动的复合扰动分类识别问题,提出了一种基于分段改进S变换和RBF神经网络相结合的复合电能质量扰动识别新方法。首先对离散S变换进行了分段改进,将时域分辨率和频域分辨率进行分段处理,通过分析改进S变换得到的模时频矩阵,绘制了能够反映扰动信号不同突变参数的特性曲线。其次利用统计方法优化计算提取了10种用于模式识别的特征量,并用局部逼近的RBF神经网络设计了分类器对提取的特征样本进行训练和分类,最后在不同噪声环境下对5种单一扰动及谐波+电压暂降、电压暂降+闪变等6类复合电能质量扰动的分类识别进行了仿真验证。仿真结果表明,该方案时频处理、分类能力和学习速度等方面均优于普通改进S变换+全局逼近网络的方法,且鲁棒性强,能准确识别多种单一扰动及两种扰动同时存在的复合电能质量扰动。     

三相电压波形校正智能控制有源滤波器

为了使电网电压尽可能接近正弦波形,提出了一种通过校正网侧电压波形来减小电网中的谐波电流的智能控制有源滤波技术.智能芯片检测电网电压波形,再与标准正弦电压波形比较求得误差电压,该误差电压中含有电网中谐波电流的波形信息.用这个误差电压形成PWM整流器的指示电流,有源滤波器在智能芯片的控制下从电网吸收谐波电流并向电网输出基波...     

适用于电气化铁路的单相注入式混合有源滤波器

提出了一种适用于电气化铁路的新型单相注入式混合有源滤波器。这种新型有源电力滤波器能够补偿一定的无功,注入支路通过添加基波谐振支路的方法大大降低了有源滤波器的容量,克服了电网基波电压对装置的影响。并详细分析了系统的滤波原理,提出了基于卡尔曼增益自调整的改进型动态谐波含量估计方法,能够快速准确的跟踪检测电网谐波电流,并且克服了电力机车等冲击性负荷引起的电网电压畸变对谐波检测精度的影响;提出了基于逆变器两侧能量平衡的电流控制方法使有源部分吸收或释放一定有功或无功功率及产生与注入支路回灌谐波电流相反的抑制电流,然后联合电网谐波电流跟踪控制以获得系统参考信号,保证了直流侧电压的稳定,提高了装置可靠性,增强了系统滤波性能。仿真和实验结果表明这种新型单相注入式混合有源电力滤波器可靠性较高,满足电气化铁路所带电力机车等冲击性负荷的要求,可获得良好的滤波效果。     

基于改进的RBAUKF的电力频率跟踪新算法

将电力系统的三相电压信号构造成一复信号,然后利用改进的RBAUKF算法对发生谐波和随机噪声干扰的电力系统电压信号进行动态估计和频率跟踪。针对电力系统中充满谐波畸变和随机噪声干扰,而复数型扩展卡尔曼滤波(ECKF)只是对非线性映射本身做某种线性近似等问题,在基于无迹卡尔曼滤波(UKF)算法的基础上,提出改进的无迹卡尔曼滤波(RBAUKF)的估计算法,并与复数型扩展卡尔曼滤波做了相应比较。经过对几种暂态电力系号模型的仿真试验表明,改进的RBAUKF在计算复杂度和估计精度上都优于ECKF。     

基于卡尔曼滤波和深度置信网络的复合电能质量扰动分类

针对实际电能质量扰动数据大、识别多重扰动精度不高的问题,提出了一种基于自适应最大似然卡尔曼滤波和深度置信网络相结合的电能质量扰动识别方法.首先,该方法使用自适应最大似然卡尔曼滤波对含有噪声的原始扰动信号进行去噪.然后,通过深度置信网络对去除噪声的扰动信号进行训练、分类,以此实现电能质量扰动类型的识别.最后,在20类不同...     

强跟踪UKF算法在光伏系统MPPT中的应用

强跟踪UKF算法是采用Unscented策略逼近非线性分布且强跟踪系统突变的状态估计算法,该算法兼具强跟踪算法鲁棒性强、Unscented变换精度高、实现简单的优点。针对光伏系统在部分遮蔽情况下最大功率点误判问题,结合恒压法与强跟踪UKF算法,提出了一种新的光伏系统MPPT策略。采用恒压法快速定位最大功率点的电压范围;在该电压范围用尽量小步长的控制电压,以相应瞬时功率作为被估量,采用STUKF算法精确估算最大功率点,确定相应控制电压;保证光伏系统MPPT跟踪速度基础上,提高跟踪精度,通过状态跟踪判断状态突变,避免了误判问题。仿真和实验验证了所提策略的正确性、有效性。     

Non-recursive methods for on-line estimation of voltages or currents and symmetrical components using Kalman filter theory

Using Kalman filter theory, new non-recursive algorithms for estimating the fundamental voltage and current waveforms from noise signals are developed and investigated. For non-harmonic random noise, these procedures have much better filter properties than Fourier algorithms, especially with increase in filter order and sampling frequency. The properties of the new methods in the time domain are also better than the Fourier ones. New algorithms for estimation of the symmetrical components are also developed and investigated. The use of the elaborated algorithms for protecting electric power systems has some advantages, owing to the convenient transient response time.     

基于数字PI控制的燃料电池电站功率研究

根据燃料电池电站功率逆变系统非线性的特点,进行线性化处理.采用数字化PI控制其输出波形.采用带滤波电感电流内环的电压瞬时值控制策略,电压瞬时值外环反馈控制提高系统动态响应,电流瞬时值内环反馈摔制提高系统稳态输出精度,选用TI控制器软件平台试凑PI参数.实验表明,该设计方案快捷、有效,电压输出波形良好.