电参量自适应测量技术研究
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摘要
傅里叶变换是常用的电参量分析方法,本文在分析傅里叶变换的特点和不足的基础上,将神经网络和功率谱估计应用于电参量测量,提出增强型Adaline神经网络和谐波基函数神经网络两种自适应电参量分析模型,以适应更短采样数据、更高频率分辨率、更快学习速度的电参量测量要求。
傅里叶变换在电参量测量中的局限性在于它存在频谱泄漏和栅栏效应,频率分辨率受采样数据长度限制,以及无法直接应用于非稳态周期信号的测量。针对非稳态周期信号,提出基于搜索的同步化算法,通过逆向搜索在非同步采样数据中截取整周期的采样序列,然后通过离散傅里叶变换得到所需电参量。针对稳态周期信号的非同步测量,基于Hanning窗与基于Blackman和Blackman-Harris等窗函数的插值傅里叶变换方法存在测量精度与运算量之间的矛盾,提出改进的Hanning窗插值傅里叶变换方法,通过消除基波对二次谐波的频谱泄漏,提高了Hanning窗插值傅里叶变换的总体分析精度,而附加的运算量很小。
Adaline神经网络应用于电参量分析时通过自适应滤波原理进行测量,无需事先对神经网络权值进行样本训练,但要求信号频率事先已知,较小的频率偏差会引起较大的分析误差。提出改进的增强型Adaline神经网络和谐波基函数(HBF)神经网络模型,将基波频率作为待定的权值,可以同时估计信号频率、幅值和相位。在学习算法中增加动量项并采用频率延迟调整策略,提高了算法的收敛速度。分析自适应电参量分析算法的收敛性条件,讨论学习率和动量因子等参数对算法收敛性的影响,给出学习率和动量因子的优化选择范围。仿真算例表明增强型Adaline和HBF神经网络电参量分析方法具有分析精度高、收敛速度快的优点。
针对傅里叶变换在分析间谐波时存在频率分辨率低的不足,提出现代功率谱估计与自适应神经网络相结合的间谐波分析方法。先通过AR Burg算法或MUSIC算法估算信号中谐波和间谐波的个数及频率初值,然后应用增强型Adaline和HBF神经网络分析谐波和间谐波的频率、幅值和相位。仿真算例表明现代功率谱估计与自适应神经网络相结合的间谐波分析方法具有频率分辨率高、分析精度高、收敛速度快的优点,适用于非同步采样、数据长度较短等情况下的间谐波分析。
最后通过电参量数据采集系统对计算机用电和几种家用电器用电进行了采样,应用自适应神经网络方法进行了电参量分析,验证了所提出的自适应电参量测量方法的有效性和适用性,并得出了计算机和几种家用电器的用电数据及用电特点。
Fourier transform is the commonly used method for the analysis of electrical parameters. The paper analyzed the characteristics and laps of the Fourier transform and introduced the neurual network and power spectral density (PSD) estimation in the measurement of the electrical parameters. Two adaptive electrical parameter analysis models, enhanced Adaline neural network and harmonic basis function neural network, were proposed to meet up to the electrical measurement requirement of shorter sample data, higher frequency resolution and faster learning speed.
The limitation of Fourier transform in the measurement of electrical parameters is that it exists the spectral leakage and fence effect, its frequency resolution is decided by the sample data length, and it can not be directly applied to non-stationary periodic signal measurement. For the non-stationary periodic signal, the paper presented a searching based synchronizing approach. In the approach, the integer cycles of sequence were truncated from the asynchronous sample data by reverse searching method, and then the electrical parameters were obtained by discrete Fourier transform. For the asynchronous measurment of the stationary periodic signal, there are conflicts between the measurement precision and the computational burden for the windowed interpolating Fourier transform methods such as Hanning, Blackman or Blackman-Harris window. The paper presented an improved Hanning windowed interpolating Fourier transform method which eliminated the spectral leakage on the second hamonic generated by the fundamental component. The improved method promoted the orerall analytical accuracy of the Hanning interpolating Fourier transform with a small amount of additional computation.
Adaline neural network measures electrical parameters through the principle of adaptive filtering, without prior sample training of the neural network weights. But Adaline requests the signal frequency known in advance, otherwise small frequency deviation may generate large analytical error. The paper proposed improved enhanced Adaline neural network and harmonic basis function (HBF) neural network models, which treated the fundamental frequency as the weight to be determined, so as to estimate the frequency, amplitudes and phases at the same time. The momentum and the delayed frequency adjustment were adopted in the learning algorithms to promote the convergence speed. The convergence conditions of the adaptive algorithms were analyzed, the impacts of the learning rates and the momentum factors on the algrithm convergence performances were discussed, and their optimal ranges of choices were presented. The simulation examples demonstrated that the enhanced Adaline neural network and the HBF neural network approaches achieved high precision and rapid convergence.
An interhamonic analysis method of combining modern PSD emstimation with the adaptive neural network was presented to overcome the disadvantage of Fourier transform of low frequency resolution. In the supposed method, AR Burg algorithm or MUSIC algorithm was used to estimate the numbers and the pre-estimated frequencies of harmonics and interharmonics, and then all the frequencies, amplitudes and phases were obtained by the enhanced Adaline or HBF neural network. The simulation results showed that the proposed method had the advantages of high frequency resolution, good analytical accuracy and fast convergence speed, and was applicable for interhamonic analysis of asynchronous sampling and short data length.
Finally, the electrical comsuptions of computers and several home appliances were sampled through an electrical data acquisition system, and their electrical parameters were analyzed by the adaptive neural network method. The analysis results further verified the validity and applicability of the proposed adaptive measurement methods for electrical parameters, as well as obtained the electricity comsuption data and characteristics of computers and several home appliances.
傅里叶变换在电参量测量中的局限性在于它存在频谱泄漏和栅栏效应,频率分辨率受采样数据长度限制,以及无法直接应用于非稳态周期信号的测量。针对非稳态周期信号,提出基于搜索的同步化算法,通过逆向搜索在非同步采样数据中截取整周期的采样序列,然后通过离散傅里叶变换得到所需电参量。针对稳态周期信号的非同步测量,基于Hanning窗与基于Blackman和Blackman-Harris等窗函数的插值傅里叶变换方法存在测量精度与运算量之间的矛盾,提出改进的Hanning窗插值傅里叶变换方法,通过消除基波对二次谐波的频谱泄漏,提高了Hanning窗插值傅里叶变换的总体分析精度,而附加的运算量很小。
Adaline神经网络应用于电参量分析时通过自适应滤波原理进行测量,无需事先对神经网络权值进行样本训练,但要求信号频率事先已知,较小的频率偏差会引起较大的分析误差。提出改进的增强型Adaline神经网络和谐波基函数(HBF)神经网络模型,将基波频率作为待定的权值,可以同时估计信号频率、幅值和相位。在学习算法中增加动量项并采用频率延迟调整策略,提高了算法的收敛速度。分析自适应电参量分析算法的收敛性条件,讨论学习率和动量因子等参数对算法收敛性的影响,给出学习率和动量因子的优化选择范围。仿真算例表明增强型Adaline和HBF神经网络电参量分析方法具有分析精度高、收敛速度快的优点。
针对傅里叶变换在分析间谐波时存在频率分辨率低的不足,提出现代功率谱估计与自适应神经网络相结合的间谐波分析方法。先通过AR Burg算法或MUSIC算法估算信号中谐波和间谐波的个数及频率初值,然后应用增强型Adaline和HBF神经网络分析谐波和间谐波的频率、幅值和相位。仿真算例表明现代功率谱估计与自适应神经网络相结合的间谐波分析方法具有频率分辨率高、分析精度高、收敛速度快的优点,适用于非同步采样、数据长度较短等情况下的间谐波分析。
最后通过电参量数据采集系统对计算机用电和几种家用电器用电进行了采样,应用自适应神经网络方法进行了电参量分析,验证了所提出的自适应电参量测量方法的有效性和适用性,并得出了计算机和几种家用电器的用电数据及用电特点。
Fourier transform is the commonly used method for the analysis of electrical parameters. The paper analyzed the characteristics and laps of the Fourier transform and introduced the neurual network and power spectral density (PSD) estimation in the measurement of the electrical parameters. Two adaptive electrical parameter analysis models, enhanced Adaline neural network and harmonic basis function neural network, were proposed to meet up to the electrical measurement requirement of shorter sample data, higher frequency resolution and faster learning speed.
The limitation of Fourier transform in the measurement of electrical parameters is that it exists the spectral leakage and fence effect, its frequency resolution is decided by the sample data length, and it can not be directly applied to non-stationary periodic signal measurement. For the non-stationary periodic signal, the paper presented a searching based synchronizing approach. In the approach, the integer cycles of sequence were truncated from the asynchronous sample data by reverse searching method, and then the electrical parameters were obtained by discrete Fourier transform. For the asynchronous measurment of the stationary periodic signal, there are conflicts between the measurement precision and the computational burden for the windowed interpolating Fourier transform methods such as Hanning, Blackman or Blackman-Harris window. The paper presented an improved Hanning windowed interpolating Fourier transform method which eliminated the spectral leakage on the second hamonic generated by the fundamental component. The improved method promoted the orerall analytical accuracy of the Hanning interpolating Fourier transform with a small amount of additional computation.
Adaline neural network measures electrical parameters through the principle of adaptive filtering, without prior sample training of the neural network weights. But Adaline requests the signal frequency known in advance, otherwise small frequency deviation may generate large analytical error. The paper proposed improved enhanced Adaline neural network and harmonic basis function (HBF) neural network models, which treated the fundamental frequency as the weight to be determined, so as to estimate the frequency, amplitudes and phases at the same time. The momentum and the delayed frequency adjustment were adopted in the learning algorithms to promote the convergence speed. The convergence conditions of the adaptive algorithms were analyzed, the impacts of the learning rates and the momentum factors on the algrithm convergence performances were discussed, and their optimal ranges of choices were presented. The simulation examples demonstrated that the enhanced Adaline neural network and the HBF neural network approaches achieved high precision and rapid convergence.
An interhamonic analysis method of combining modern PSD emstimation with the adaptive neural network was presented to overcome the disadvantage of Fourier transform of low frequency resolution. In the supposed method, AR Burg algorithm or MUSIC algorithm was used to estimate the numbers and the pre-estimated frequencies of harmonics and interharmonics, and then all the frequencies, amplitudes and phases were obtained by the enhanced Adaline or HBF neural network. The simulation results showed that the proposed method had the advantages of high frequency resolution, good analytical accuracy and fast convergence speed, and was applicable for interhamonic analysis of asynchronous sampling and short data length.
Finally, the electrical comsuptions of computers and several home appliances were sampled through an electrical data acquisition system, and their electrical parameters were analyzed by the adaptive neural network method. The analysis results further verified the validity and applicability of the proposed adaptive measurement methods for electrical parameters, as well as obtained the electricity comsuption data and characteristics of computers and several home appliances.
引文
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