| Abstract:The current anti-electric theft monitoring methods in sensitive stations face continuous monitoring conditions, and the monitoring data contains a large amount of noise data and useless data, resulting in insufficient diagnosis basis for electric theft behavior. To solve this problem, a recursive wavelet neural network is proposed. Anti-theft monitoring method in sensitive station area. Use A/D acquisition circuit and transformer to collect user electricity data, remove noise data and useless data through data preprocessing, and normalize the data at the same time. On this basis, the characteristics of electricity consumption data are extracted, and the identification indicators of electricity theft behavior are determined from four aspects of current, voltage, power factor, and electricity. The data and feature values are input into the recursive wavelet neural network, and the identification indicators are combined to output accurate． The result of discrimination of electricity theft conducts anti-electricity theft monitoring in sensitive stations. The experimental results show that the research method can accurately capture the power factor changes, the time for discriminating power theft behavior is short, and the anti-power theft monitoring effect is better in the sensitive area. |
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