A Compressive Sensing-Based Centralized Measurement Method for Electrical Energy Data in Substations

With the widespread integration of nonlinear loads and new energy sources, the impact of system frequency fluctuations and harmonic pollution on the accuracy of substation metering is becoming increasingly severe, and the amount of metering data is surging. The data storage and transmission of the next-generation new centralized metering methods for electric energy are facing severe challenges. By applying compressed observation and algorithm reconstruction to centralized measurement systems, addressing the issue of reconstruction measurement distortion caused by frequency fluctuations and harmonic pollution in the front-end system of centralized measurement, using convolutional windows to observe wide-area power data, compressing measurement messages for unified transmission, and using algorithms for reconstruction in the back - end, and thereby mastering the measurement information of the entire station. Simulation analysis and experimental verification show that the method proposed in this article effectively reduces the storage space and transmission cost required for long - term and large - scale measurement data. It also has high measurement accuracy and stability, providing a feasible path for the application of centralized energy measurement architecture in the station.