Power Quality Disturbance Analysis Based On Iterative Filtering And Its Improved Method

As the important carrier of energy development and consumption,electric energy is the core of modern energy system.The stability and reliability of power quality directly affect social and economic development,and also affect the interest relationship among the three parties of power generation,power supply and power consumption.As more and more distributed generation systems are integrated into the grid and non-linear power electronics are widely used,power quality problem is becoming increasingly prominent.In order to quickly and accurately extract the characteristic information of power quality disturbance signal for subsequent governance and improvement,this paper studies power quality disturbance analysis based on iterative filtering and its improvement method,the main research work is:Event-based and continuous power quality disturbances and characteristic analysis indexes are studied.In terms of disturbance types,we focus on event-based power quality and harmonic distortion,detailing their definitions,causes and hazards,and simulating the corresponding numerical models.The characteristic information of the disturbance event is studied in combination with the calculation example,and the five characteristic indexes of disturbance time localization,RMS,spectrum,phase jump and instantaneous frequency are explored and used as a set of power quality disturbance analysis system.Aiming at the problems of traditional disturbance analysis methods such as large time positioning deviation,easy modal mixing and long analysis time,the power quality disturbance analysis based on iterative filtering(IF)method is studied in detail from the aspects of algorithm principle,parameter selection and decomposition effect,and its decomposition performance is greatly improved in terms of speed and effect compared with traditional methods.Further,the fast Fourier transform is used to improve the IF and construct the fast iterative filtering algorithm(FIF),which reduces the complexity of the operation with the same accuracy and significantly alleviates the boundary effect.Engineering applications show that iterative filtering and fast iterative filtering have good accuracy in extracting feature indicators such as perturbation start/stop time,EMS and phase jump,withFIF having smaller boundary effects and requiring less time.In order to further improve the decomposition speed and accuracy ofFIF and fit the goal of real power quality instant analysis,two improved methods based on fast iterative filtering-direct fast iterative filtering(dFIF)and hard threshold fast iterative filtering(htFIF)-are proposed.The dFIF calculates each intrinsic mode component in one step by pre-calculating the number of iterations.The htFIF reduces the computational parameters compared to the dFIF,further increasing the speed of the operation.The performance of the two methods is analyzed by numerical experiments,in which dFIF is more accurate in time localization and htFIF is faster in decomposition,and both are better thanFIF in terms of perturbation time localization,transient separation effect and operation time.Applying the dFIF and htFIF methods to actual disturbance events,both can quickly and accurately extract disturbance features,providing a new solution for immediate analysis of power quality disturbances.In order to improve the separation effect of iterative filtering,fast iterative filtering and their improvement methods on high frequency components,to obtain higher accuracy and enhance robustness,and to improve the boundary effect problem of traditional adaptive iterative filtering methods,a sinusoidal extended adaptive iterative filtering(SEALIF)for power quality disturbance analysis is proposed.This method introduces the Fokker-Planck differential equation and sinusoidal extended of the signal based on the iterative filtering method to construct a filter that can be adaptively adjusted,while significantly reducing the boundary effect.The power quality disturbance characteristic indexes are analyzed through numerical experiments and engineering applications,showing that SEALIF has stronger anti-aliasing and anti-disturbance capability as well as more accurate decomposition performance,which is especially suitable for complex disturbances and small disturbances in practical engineering applications.