基于自适应陷波器的电网频率估计方法

本文针对电网频率难以精确测量的问题,提出利用自适应陷波器结构的滤波功能,结合电网电压基波信号及其谐波信号的特性,以陷波器实时输出平方值作为陷波器参数的优化目标函数,根据随机优化理论推导出陷波器参数在线迭代算法,从而对与频率有关的陷波参数进行自适应调整,达到电网频率估计的目的。该方法结构简单,通过仿真分析和实验测试,检验了所提出的频率估计算法的收敛速度和逼近精度,表明基于自适应陷波器的电网频率估计方法,可以准确估计出电网频率。     

An adaptive neural network approach for the estimation of power system frequency

A new approach to the estimation of power system frequency using an adaptive neural network is presented in this paper. This approach uses a linear adaptive neuron or an adaptive linear combiner called “Adaline” to identify the parameters of a discrete signal model of the power system voltage. Here, the learning parameters are adjusted to force the error between the actual and the computed signal samples to satisfy a stable difference error equation, rather than to minimize an error function. The proposed algorithm shows a high degree of robustness and estimation accuracy over a wide range of frequency changes. The technique is shown to be capable of tracking power system conditions and is immune to the effects of harmonics and random noise.     

Fast frequency and harmonic estimation in power systems using a new optimized adaptive filter

This paper presents an adaptive filter for fast estimation of frequency and harmonic components of a power system voltage or current signal corrupted by noise with low signal to noise ratio (SNR). Unlike the conventional linear combiner (Adaline) approach, the new algorithm is based on an objective function often used in independent component analysis for robust tracking under impulse noise conditions. However, the accuracy and speed of convergence of this algorithm depend on the choice of step size of the filter and its adaptation. Instead of choosing the step size η and the parameter β of the cost function by trial and error, an adaptive particle swarm optimization technique is used alternatively to obtain both η and β to reduce the error between the observed voltage or current samples and the estimated ones. Using the optimized values, the amplitude and phase of the fundamental and harmonic components are estimated. Further, the extracted fundamental component is used to estimate any frequency drift of the power system recursively using an optimized error function obtained from three consecutive voltage samples. To test the effectiveness of the algorithm, several time-varying power system signals are simulated with harmonics, interharmonics, and decaying dc components buried in noise with low signal-to-noise ratio (SNR) and are used to estimate the frequency and harmonic components. This approach will be useful in islanding detection of a distributed generating system.     

一种电力系统频率的实时估算方法

提出了一种曲线拟合结合泰勒级数展开的电力系统实时频率测量算法,采用分子、分母绝对值相加的方法来避免了计算过程中分母过零点的影响,理论分析及数字仿真表明,该算法实现简单,有很高的计算精度,对谐波也有一定的抑制能力.完全能满足电力系统实时频率估算的要求.     

一种电力系统频率的实时估算方法

提出了一种曲线拟合结合泰勒级数展开的电力系统实时频率测量算法,采用分子、分母绝对值相加的方法来避免了计算过程中分母过零点的影响,理论分析及数字仿真表明,该算法实现简单,有很高的计算精度,对谐波也有一定的抑制能力。完全能满足电力系统实时频率估算的要求。     

Power system frequency estimation using least mean square technique

Frequency is an important parameter in power system monitoring, control, and protection. A least mean square (LMS) algorithm in complex form is presented in this paper to estimate power system frequency where the formulated structure is very simple. The three-phase voltages are converted to a complex form for processing by the proposed algorithm. To enhance the convergence characteristic of the complex form of the LMS algorithm, a variable adaptation step-size is incorporated. The performance of the new algorithm is studied through simulations at different situations of the power system.     

基于自适应无迹卡尔曼滤波的动力电池SOC估计

针对传统无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计电池SOC时,在未知的干扰噪声条件下滤波精度较低和稳定性较差等问题,基于等效的二阶RC电路模型,提出自适应无迹卡尔曼滤波(adaptive unscented Kalman filter,AUKF)算法.在模型参数辨识的基础上,构建...     

Power system frequency estimation using a least mean squares differentiator

The estimation of the power system frequency plays an important role in power quality assessment and power flow control. This paper presents a frequency estimator based on a moving average filter and on a least mean squares differentiator applied to the phase-angles of the grid voltages. The phase-angles are obtained using the Clarke transformation. The estimator achieves a high rejection to the grid disturbances such as unbalances and harmonics. A recursive version is proposed allowing an efficient computational implementation. The estimator performance is validated and compared with four other estimators by several simulations.     

电力系统低频振荡频率的快速估计算法

为克服当前电力系统稳定器PSS（Power System Stabilizer)参数固定，运行方式单一的缺点，设计了一种实用的算法，能够在振荡在1／4周期内准确地估计出振荡频率，对PSS的运行参数进行实时调整，使所设计的PSS能够灵活地抑制各种频率的振荡，该算法运用了非线性回归法，首先分析了低频振荡波形遵循的数学规律，提出合适的回归方程，并通过泰勒展开法，将该回归方程进行简化，再等间隔抽取N个采集点进行回归计算，求出回归方程系数，减少了运算量，然后使用得到的系数技术振荡频率，最后利用Matlab对该算法进行仿真，结果表明该算法具有较高的精度和速度。     

基于惯量响应支撑功率的电力系统一次调频功率估算

随着新能源渗透率不断提高以及高压直流电量HVDC的大规模应用,电力系统惯量响应与一次调频响应的能力不断减弱,威胁电力系统的频率稳定.目前的研究工作主要针对于惯量响应的估算、新能源发电参与惯量响应和一次调频的控制策略设计,并未展开电力系统一次调频功率估算的研究.论文提出了基于惯量响应支撑功率的电力系统一次调频功率估算.阐...     

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     

电力系统频率响应的改进模型与参数估计

近年来,多种因素导致电力系统频率大波动事故时有发生。而目前对电力系统调频能力预估误差较大,有必要采用电力系统频率响应(SFR)模型计算频率的动态响应。文中分析了SFR经典模型的不足之处,据此改进了SFR模型结构,其中考虑了等值调速器的动态特性。进而提出了SFR改进模型的参数估计方法,先直接计算获得部分参数,然后加入保证稳态一致性的参数约束条件,最后辨识获得其余参数。通过仿真算例对频率响应进行了计算,验证了SFR改进模型结构与参数估计方法的有效性。结果表明,SFR改进模型能够有效表征系统频率响应的主要指标,其精度显著高于SFR经典模型。     

A signal processing adaptive algorithm for nonstationary power signal parameter estimation

This paper presents the design and analysis of an adaptive algorithm for tracking the amplitude, phase and frequency of the fundamental, harmonics and interharmonics present in time‐varying power sinusoid in white noise. If frequency, amplitude and phase of the multiple sinusoids become nonstationary, they are estimated as an unconstrained optimization problem using robust and low complexity multi‐objective Gauss–Newton algorithm. The presented algorithm deals with frequency drift and can accurately estimate frequency variation, amplitude and phase variation, as well as harmonic amplitude and phase variations. Further, the learning parameters in the proposed algorithm are tuned iteratively to provide faster convergence and better accuracy. The excellent tracking capability of proposed multi‐objective Gauss–Newton algorithm is shown through simulation and experimental results in a nonstationary environment. Copyright © 2012 John Wiley & Sons, Ltd.     

A study of the least mean p‐th adaptive notch filter

In this paper we propose a least mean p‐th adaptive notch filter that has a cost function of E[e^p(n)], where e(n) is the estimation error. The structure of the adaptive filter is a tandem connection of the second‐order adaptive notch filter with an all‐pass filter. In general, the bandwidth of the notch filter should be extremely small from the theoretical and practical viewpoints. However, the convergence speed of the weight becomes slow if the bandwidth is reduced. The transfer function of the notch filter has the following special characteristic, that is, zero in the center frequency and unity in other frequencies. The equivalent broad bandwidth can be obtained when the cost function is chosen as E[e^p(n)]. Higher convergence speed and excellent stationary performance are obtained using the combination of E[e^p(n)]. Finally, the convergence performance of the estimation accuracy is verified by the computer simulation. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(3): 46–53, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20045     

Improved Recursive Newton Type Algorithm based power system frequency estimation

This paper presents power system frequency estimation by using an Improved Recursive Newton Type (IRNTA) algorithm. The proposed approach uses Jacobian and covariance matrices for updating the unknown parameters. The recursive form of unknown parameters and covariance matrix are incorporated in the algorithm to have faster convergence. The performance of the proposed algorithm is studied through simulations and experiments for several critical cases that often arise in a power system. Efficacy of the proposed algorithm is also compared with other signal processing techniques such as Recursive Least Square (RLS) and Kalman Filter (KF). Studies made on industrial data also support for the superiority of the proposed algorithm.     

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     

LCL型并网逆变器新型频率自适应重复控制方法

提出一种应用于LCL型并网逆变器的新型频率自适应重复控制(FARC)方法。该方法采用全通滤波器来替代重复控制器中频率比值小数部分构成的延时单元。当电网频率变化时,通过在线调整全通滤波器的系数,使重复控制器内模的谐振频率逼近电网基波及谐波频率的实际值,从而实现在电网频率变化情况下对电压谐波的有效抑制,进而控制逆变器输出高质量的并网电流。详细给出FARC的设计过程及系统的稳定性分析。仿真和实验结果验证了所提控制方法的有效性和可行性。     

Wide-range,fast and robust estimation of power system frequency

A novel method of frequency estimation, for power system applications such as control and protection, is proposed and its performance is evaluated. The proposed frequency estimator can measure small as well as large deviations from the nominal point. It closely follows step, ramp and oscillatory variations of the frequency over time. Other significant features of the proposed algorithm are: (a) structural simplicity, which renders it suitable for hardware/software implementation; (b) performance robustness in the presence of DC offset and harmonic components; (c) noise immunity; (d) performance robustness with respect to external disturbances such as commutation notches and switching transients; and (e) flexibility of control over speed and accuracy. Rate of change of frequency is also directly provided by the estimator which is a requirement in some system protection algorithms. In a highly polluted environment, the proposed estimator can be set to measure the frequency in few cycles of the signal and with a steady-state error which is limited to 0.02 Hz.     

A weighted least squares method for determination of power system equivalents

The effect of several important factors, like the acceleration of convergence, selection of initial gradient, choice of parameter models, selection of output variables, etc., on the convergence of the parameters of a power system equivalent is investigated. It is observed that a fifth-order parameter model with two output variables provides little faster convergence near a minimum compared to the reduced-order models. The reduced-order models with lower outputs are computationally efficient and provide reasonable values for the more important parameters. A check on the stability of the model has been proposed to save further computation time.