Research On Power Quality Disturbance Detection Technology Based On Fusion And Parameter Optimization

With the integration of distributed power generations and the increasing number of nonlinear,impact and sensitive loads connected,resulting in distortion of current and voltage,the decline in power quality will inevitably cause users to suffer unnecessary economic losses.If the power quality disturbance can be detected and feature extraction can be detected,and the power quality can be assessed through the disturbance characteristic parameters,it is of great significance to the evaluation and treatment of power quality.Based on the previous research and analysis,this paper will deeply study the power quality disturbance detection technology based on fusion and parameter optimization.The main contents of this article are as follows:First,the basic principles,implementation methods and characteristics of the wavelet threshold denoising,CEEMDAN,VMD and Teager energy operators are analyzed.Among them,wavelet threshold denoising has excellent noise reduction performance,which can effectively improve the signal-to-noise ratio of the signal,but it has non-adaptive problems,and the threshold and threshold function cannot be adjusted adaptively according to the noise distribution.The problems of difficult averaging processing of CEEMDAN and non-adaptive VMD parameter presets in low signal-tonoise ratio environments are obtained,which provides a theoretical basis for fusion and parameter optimization.Secondly,a power quality disturbance detection technology based on improved wavelet threshold denoising and CEEMDAN fusion is proposed.The adaptive correction threshold and adjustable threshold function are used to improve the wavelet threshold denoising,making it adaptive,which can accurately estimate the noise in multiple types of power quality disturbance signals,and greatly improve the signal-to-noise ratio.The fusion of the two can greatly improve the detection accuracy of CEEMDAN in low signalto-noise ratio environments,and also significantly broaden the scope of application of CEEMDAN.Then,a power quality disturbance detection technique based on parameter optimization VMD is proposed.The use of Tianniu su search algorithm to optimize the combination of VMD parameters,and the function combining envelope entropy and the number of iterations as the fitness function can fully quantify the decomposition effect of VMD and ensure the real-time performance of VMD.The optimized VMD parameter combination can be adaptively adjusted according to the changes of perturbation characteristics to achieve accurate division of the frequency band of the perturbed signal,and effectively overcome the over-decomposition and under-decomposition problems caused by poor VMD parameter presets.Finally,in order to verify the difference in accuracy between the power quality disturbance detection techniques based on fusion and parameter optimization,a power system model containing distributed power generation is constructed,and the power quality disturbance characteristics reflected in the system model under different switching operations are used as experimental samples,and the above two detection techniques are used to detect and compare them.Experimental results show that the parameter optimization VMD has strong practical applicability and compatibility advantages,and the detection error is less than 1%.