Research On Frequency Characteristics Of Power Grid Based On Synchronous Measurement Data Of Distribution Network

With the rapid development of large-scale AC/DC interconnected power grids in China and the large-scale integration of new energy,the frequency characteristics of different regions of the power system have undergone significant changes.From the observation of measured frequencies,it is found that the specific manifestation is that the frequency of power grids in different regions exhibits common characteristics of consistent waveforms;Each regional power grid has unique spatiotemporal distribution characteristics.Therefore,extracting and identifying frequency features can provide a foundation for understanding power grid characteristics and further analysis of frequency control,network security,and other aspects.This article is based on a large amount of measured data from the Full view Synchronized Measurement System(SYMS),and focuses on the analysis of frequency characteristics of regional power grids.It conducts research from three aspects:common frequency characteristics of multi regional power grids,spatiotemporal distribution characteristics of regional power grid frequencies,and identification of frequency characteristics of regional power grids.The main work and innovative points of the paper are as follows:In response to the common characteristics of frequency performance in different regional power grids,a large amount of measured frequency data in the North China Central China East China region was analyzed.A method combining frequency heat map and convolutional neural network was proposed to analyze the distribution characteristics of daily frequency data at different time periods.The convolutional neural network was used to compare the frequency distribution heat map of multiple regional power grids,confirming the objective existence of common characteristics in regional power grids;A frequency commonality feature representation method based on short time window Pearson correlation coefficient was proposed,which quickly quantifies the strength of commonality features between regional power grid frequency data using Pearson correlation coefficient,achieving the effect of multi regional power grid frequency data clustering.Aiming at the problem that it is difficult to extract the time-space distribution characteristics of regional power grid,a frequency distribution feature extraction method based on mathematical morphology is proposed;A method for extracting and analyzing frequency noise signals based on wavelet transform was proposed,and the characteristics of noise signals were analyzed.On this basis,a feature extraction method based on variational modal decomposition was proposed,which extracted high-latitude features from the characterization of power grid characteristics in frequency time-frequency domain.This clarified the relationship between noise like components and regional power grid characteristics,and successfully used frequency spatiotemporal distribution features as "frequency fingerprint" regional power grid feature identification.Regarding the identification of frequency features in regional power grids,especially the synchronization of frequency data with common features,it is difficult to identify the features.A "frequency fingerprint" identification algorithm based on the combination of variational modal decomposition and deep residual network(ResNet)is proposed based on frequency commonality and spatiotemporal distribution characteristics.The algorithm identifies the source of frequency data based on the degree of matching between the "frequency fingerprint" of each regional power grid and unknown frequency data.Further testing and analysis of measured frequency signals from multiple cities such as Beijing and Changzhi were conducted,and a cross mixed frequency test was constructed for Beijing and Nanjing to verify the effectiveness of the proposed method.