KEYWORDS: Data modeling, Reconstruction algorithms, Support vector machines, Instrument modeling, Evolutionary algorithms, Measurement devices, Education and training, Detection and tracking algorithms, Statistical analysis, Analytical research
When identifying abnormal timing of power metering devices, the identification effect is poor due to the diverse attributes of the original power metering device state data. Therefore, a research on abnormal timing identification algorithm of power metering devices based on variational autoencoder and support vector machine is proposed. A VAE-LSTM- DTW model was constructed with a variational autoencoder as the core, which can be mainly divided into two parts. The reconstruction model is composed of a VAE network improved by LSTM, which is responsible for generating the time series reconstruction data of the power metering device corresponding to the input data. The evaluation model is responsible for comparing the reconstruction effect of the model on the input power metering device status data through DTW and algorithm, and performing anomaly detection accordingly. When identifying abnormal time series, support vector machines are used to match and identify the abnormal features of the operating state data of individual power metering devices. In the test results, the identification accuracy of the design algorithm is stable at above 0.85, the recall rate is stable at above 0.80, and the F1 score is stable at above 0.90.
With the automatic verification of intelligent power meters, manpower is saved and the verification efficiency is improved. However, the large-scale automatic verification method can not meet the parameter ratio specified in the verification regulation because the verification environment is difficult to control. The environmental factors in the verification are difficult to be accurately controlled, which leads to inaccurate verification results. There are a large number of watt-hour meters in the process of centralized verification. If the verification results are not accurate, it will lead to unnecessary economic losses. In this paper, a T distribution test model is established to consider the influence of the heat of the external shunt and the internal power chip on the temperature of the metering chip in different environments. According to the error fitting curve corresponding to the chip, the measurement error of the DC energy meter in different environments is obtained. The formula is imported into MATLAB for simulation analysis to study the factors affecting the accuracy of the metering algorithm. The error formula and influencing factors are derived when the effective value method is adopted in fast charging mode, and the simulation analysis is carried out.
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