Research on load decomposition method based on under determined blind source separation model
To achieve non-invasive monitoring and identification of household loads and lay the foundation for source load interaction of flexible loads,this paper proposes a load decomposition method not relying on prior information.The load of household users is regarded as an unknown source signal while the usage of the load is treated as an unknown superposition method.Multiple monitoring data of household electricity meters are employed as observation signals to build a blind source separation model for load decomposition.Blind source separation of household loads is achieved based on multi period power data of smart meters.Load switching is identified by subtracting adjacent points in the time series of electricity consumption.Two-dimensional distribution characteristics of load power and usage time are built.Load features are clustered and intra cluster and inter cluster dissimilarity of load features is calculated.Contour coefficient method is employed to determine the number of independent loads.Based on the built potential independent load power matrix,the usage probability of independent loads is calculated under different combinations and the true active power of independent loads is selected according to the probability normalization idea.By combining the blind source separation model,the disconnection situation of each load in different time periods is solved.Thereby,the total power signal is decomposed into the superposition of independent load powers.Non-invasive load decomposition is achieved based on meter data.Tests is conducted by using the open-source dataset REDD.Our results show the proposed method accurately identifies the number of loads and decomposes different loads for different load combination scenarios without utilizing prior knowledge.The average absolute error of load decomposition is no more than 6.8％,and the accuracy of disconnection recognition is above 0.77.These data demonstrate that our method delivers fairly good load decomposition performances and achieves blind source separation of household user loads,laying a solid foundation for flexible load recognition and regulation.
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