Power Quality Detection And Analysis Based On Local Integral Mean Decomposition Method

Hilbert-Huang Transform is now more commonly used time-frequency analysis method,which establishes a certain advantage in processing nonlinear and non-stationary signals compared with FFT analysis,wavelet transform,S transform and other methods.However,there are still some drawbacks in the process of signal processing and analysis in HHT method,so the main work of this paper is to analyze and improve shortcomings of HHT,and apply the modified HHT method to the field of power quality disturbance detection and analysis.Firstly,aiming at the problems of HHT method such as mean curve fitting,mode mixing,false component and end effect,a Local Integral Mean Decomposition(LIMD)method is proposed.In the proposed method,given the characteristics of single component signals,the single component signal with instantaneous physical meaning is redefined,which improves the effectiveness of the decomposition of intrinsic mode function.Aiming at the disadvantages of poor interpolation fitting effect and time-consuming in standard EMD method,and the way of mean curve fitting is modified.In the modified method,mean value theorem for integrals is introduced into the fitting process of mean value curve,and only one spline interpolation is utilized to construct local mean cures,in which all of the data located between the zeros and extrema are employed as local characteristic time-scale through the mean value theorem for integrals.The simulation results show that the proposed method improves the computational efficiency of the algorithm and achieves some improvement in the end effect and the false component suppression.And then,aiming at the problems LIMD method on mode mixing effect and low-resolution need to be further improved,an Adaptively Ensemble Local Integral Mean Decomposition(AELIMD)method is proposed.In the proposed method,the influence of white noise with different frequency forms on the distribution uniformity of signal extreme points is analyzed,the advantage of employing high-frequency white noises auxiliary decomposition is determined,and distribution characteristic of signal extreme points is taken as an evaluation index to adaptively select the optimal frequency of additive white-noise.Furthermore,the two key parameters respectively fixed as 0.01 times standard deviation of the original signal and two ensemble trials are deduced by investigating the principle of determining the parameters of noise in EEMD,in which the white noises is added in pairs with plus and minus signs to thetargeted signal.At last,through MATLAB simulation experiments demonstrate that the adaptability and computational efficiency of the proposed method.Finally,the modified HHT method(AELIMD method with Hilbert transform)is applied to the detection and analysis of power quality disturbance.Firstly,power quality disturbance signals with noise are decomposed to eliminate noise component by AELIMD.Furthermore,the modulus maximum points are derived by second-order derivative,which improves the accuracy of positioning disturbance time.Given high frequency compound disturbances,the two-times AELIMD decomposition method is adopted to remove noise and false components.According to steady state disturbances,the detection method is to remove harmonic components firstly and then to extract the flicker envelope.Aiming at unknown hybrid disturbances,a novel detection method based on the HHT method is presented.Simulation tests demonstrate that the feasibility and effectiveness of the proposed method.At last,The power quality disturbance experiment platform is built to simulate the real power grid disturbance faults.And experiments data measured from the platform are utilized to verify detection effect of the proposed method.In summary,the AELIMD is comprehensively demonstrated which has certain advantages in the field of power quality disturbance detection.