An online remote calibration method for smart meters based on measurement data analysis
A technology for smart meters and measuring data, applied in the field of smart meter verification in power systems, can solve problems such as rough analysis results and inability to achieve remote verification of energy meters, avoid data oversaturation, and improve analysis speed and accuracy. , the effect of improving accuracy
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Embodiment 1
[0043] An online remote calibration method for smart meters based on measurement data analysis provided by an embodiment of the present invention, see Figure 1-Figure 5 , the remote verification method includes the following steps:
[0044] S11: Obtain information required for online remote calibration of errors;
[0045] Among them, the information includes: station area and user information files, smart meter original measurement data time series.
[0046] In actual implementation, the original measurement data is mainly obtained by the smart meter data acquisition system based on AMI. The acquisition system architecture is slightly different for different regions. The physical architecture of AMI-based smart meter data acquisition is as follows: figure 2As shown, a concentrator is installed under the public distribution transformer, and the concentrator is directly connected or connected to a smart meter through a collector to realize the collection of electricity cons...
Embodiment 2
[0141] Combined with specific calculation examples, calculation formulas, Figure 6-Figure 10 Carry out feasibility verification to the remote verification method mentioned in embodiment 1, specifically include:
[0142] In order to verify the effectiveness of the proposed method, the embodiment of the present invention takes the actual smart meter measurement data of a certain city in my country from February to May 2018 as the object of analysis, and the data collection frequency is 15 minutes. Among them, the station area under study contains a total electric energy meter of the station area and 185 user electric energy meters. The no-load or light-load data is filtered out through data preprocessing, and the measurement data groups of different periods are obtained as analysis samples. Set the value of the memory length L to 1000, and use this method to solve the smart meter operation error recursive estimation curve as follows Image 6 As shown, the error analysis result...
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