The spectral leakage of the fast Fourier transform (FFT) algorithm caused by asynchronous sampling seriously affects the parameter estimation accuracy. The interpolation algorithm based on window function and the phase difference algorithm is presented in this paper to amend the FFT results by calculating the synchronous deviation. The interpolation algorithm is commonly used in harmonic analysis, but it is difficult to apply to some cosine windows with high-order or certain coefficient, because it needs to solve high-order equations to obtain the explicit interpolation formula. The Phase difference algorithm is applicable to cosine window of any order, which uses the phase difference between the discrete Fourier transform (DFT) spectrum peaks of two adjacent sampled data to estimate the synchronous deviation. Simulation results are compared and analyzed to verify the performance of the two algorithms.

Estimation Algorithms of Harmonic Parameters Based on the FFT

Multilevel inverter drivers have become widely applied in high-voltage and high-power applications. Therefore fault diagnosis of voltage source inverters is becoming more and more important. One possible fault within the inverter is IGBT open circuit fault. An overview of the different strategies to detect this fault is given, including the algorithms to localize the fault switch device. This paper presents a technique to improve the fault detection by using an algorithm of neural network with orthogonal basis functions based on recursion least square (RLS) technique. The method is used to identify the type and location of occurring faults from inverter output voltage measurement. Simulation examples of fault diagnosis by the use of the presented method were given. Simulation results have shown that the method performs satisfactorily to detect the fault type fault location and reconfiguration, so it will be very valuable in power system.

Neural network based fault diagnosis and reconfiguration method for multilevel inverter

This paper presents an algorithm of neural networks based on DFP to analyze the harmonics of power system. The main idea is to use the zero-crossing point method to calculate exact frequency, and then use DFP algorithm to train the weight of neural networks, and obtain the harmonics parameters at last. The algorithm can avoid the local least problem on grads descent method and the value problem of the learning rate. Besides, it does not involve the operation of the complex number and has a high convergence speed. The algorithm can show great advantage, when there is high harmonics. The simulating results show that the exact amplitudes and phases of high harmonics can be obtained very fast by using the algorithm.

A DFP-Neural Networks Algorithm for Analysis of Power System Harmonics

With the development of technology, the power quality detect devices become more and more powerful and the measure accuracy increase quickly. This paper analyses frequency and harmonics signal of power system based on the theory of all-phase FFT spectrum by FPGA which has powerful processing function. All-phase time-shifting phase difference correcting spectrum algorithms is used to correct the measurements of power parameters quickly. The paper analyze the real-time data of power harmonics and frequency with high accuracy, the precision of frequency is 10− 10 which can detect harmonic component under 20 degree correctly.

/pdf/detection-and-analysis-of-power-system-harmonics-based-on-2xc3vnrcke.pdf

Detection and Analysis of Power System Harmonics Based on FPGA

A new algorithm, which utilizes all-phase FFT (APFFT) and artificial neural network (ANN), is presented to detect integer harmonics and non-integer harmonics in power system. In order to improve the accuracy of harmonic parameter estimation, the method incorporates the property of phase invariant of APFFT with high speed sought optimization solution function of ANN. First, the sampled data is processed by windowed APFFT algorithm, and then some of harmonic parameters can be obtained, including the number of harmonics, accurate phases of harmonics, inaccurate magnitudes and frequencies of harmonics. Second, the number of neural nodes, the initial weights and the iterative initial parameters of base function of neural network are set according to the results analyzed with APFFT. Finally, accurate harmonic parameters can be obtained by training ANN. Simulation results demonstrate that the method can detect harmonic parameters with high precision.

http://ieeexplore.ieee.org/iel7/6642545/6643809/06643903.pdf

Harmonic Detection Method Using APFFT and Neural Network

In the paper, some problems of the current algorithms that are used to analyze the power system harmonics are pointed out FFT-Adaline algorithm for analysis of power system harmonics is presented FFT-Adaline algorithm combines the advantages of Adaline ANN algorithm with interpolated FFT algorithm with Hanning-window The new algorithm eliminates the main error factor of interpolated FFT algorithm with Hanning-window and Adaline ANN algorithm, and therefore obviously improves the accuracy of harmonic analysis The simulating result shows that the new algorithm can be applied to the precise analysis for power system harmonics By aid of the high- speeded DSP and high-powered CPU, the FFT-Adaline algorithm is quite useful in industry applications

Peng Ning-yun

Papers

An algorithm with high accuracy for analysis of power system harmonics