Harmonic Detection Partition And Control Based On Synchronous Compression Adaptive S Transform

With the increasing proportion of new energy generation and non-linear power electronics connected to the grid,the impact of harmonics and interharmonics on power quality is of general concern on both the grid side and the customer side.Harmonics can easily lead to serious safety and economic problems such as voltage deviations and resonance in industrial equipment such as transformers,motors and electric arc furnaces.The detection and treatment of harmonics and interharmonics is therefore essential to improve the safe grid connection of new energy sources and to improve power quality.A Synchrosqueezing Adaptive S-Transform(SAST)algorithm is proposed to improve the time-frequency resolution for the traditional harmonic and interharmonic detection methods,which suffer from spectral leakage,poor time-frequency resolution and weak noise immunity.Compared with the conventional S-transform,the Adaptive S-Transform(AST)can provide a higher time-frequency resolution for the Synchrosqueezing Transform(SST).At the same time,the SST compresses the time-frequency spectrum of the AST near the instantaneous frequency to the instantaneous frequency to further improve the time-frequency resolution.Compared to AST,SAST is better at handling time-varying harmonic signals with continuous frequency distribution,which can effectively improve the detection accuracy of harmonics and interharmonics.The proposed method is validated and analyzed using mathematical model signals and simulation platform signals,and the results show that the proposed method has higher detection accuracy and noise immunity,providing more accurate harmonic characteristic parameters for subsequent harmonic management.In new power systems,harmonics are characterized by wide area and decentralization,and traditional point-to-point harmonic control measures can no longer meet the demand.In this regard,this paper proposes a time-series-based similarity module degree harmonic zoning management strategy from the perspective of data correlation analysis and similarity module degree index.The strategy extracts the key feature points of the harmonic time series of each node of the power system through the sliding window processing technique,constructs the equal-dimensional sequence matrix of each node with the ninth quantile method,and uses the Euclidean distance to analyze the similarity of the equal-dimensional sequence matrix to obtain the similarity degree matrix between each node.Following this,the similarity matrix is clustered using an improved K-means clustering algorithm,while the similarity modularity index is proposed to determine the optimal number of partitions and partition results,and finally the global harmonic balance management is achieved through partition regulation.IEEE39 node simulation case analysis shows that the proposed method in this paper can effectively improve the harmonic pollution problem of the power system.