Self-adaptive stealing-leakage prevention diagnosis method

Abstract

A self-adaptive stealing-leakage diagnosis method comprises following steps of step 1: acquiring historical sample data of electric quantity, load, alarm and line loss from a metering main station; step 2: pre-processing the historical sample data; step 3: building an expert sample bank; step 4: building a stealing-leakage diagnosis model; step 5: conducting the stealing-leakage diagnosis; and step 6: optimizing model parameters, and rebuilding a model. The self-adaptive stealing-leakage prevention diagnosis method has a self-learning property, by building a model by learning power consumption data of electric power users, and reasonability and accuracy of new data are diagnosed through the formed power consumption principle, so that the monitoring and diagnosis accuracy of the stealing and leakage of electric power can be effectively improved.

Description

technical field [0001] The invention relates to a diagnosis method for preventing electricity theft and leakage in the electric power industry, in particular to a real-time automatic diagnosis method for preventing electricity theft and leakage for large users. Background technique [0002] In order to achieve the purpose of stealing electricity, electricity thieves often use various stealing methods to steal electricity. Based on the product of voltage, current, power factor and time. Therefore, as long as you find a way to change any of the three elements, you can make the meter rotate slowly, stop or even reverse, so as to achieve the purpose of stealing electricity; in addition, by changing the structure and performance of the meter itself, the meter can be slowed , can also achieve the purpose of stealing electricity. [0003] Electricity theft and failure of metering devices lead to missed collection and under-collection of electricity charges, which damage the inter...

AI Technical Summary

Problems solved by technology

[0006] Although the above diagnostic methods for anti-theft and leakage can obtain some information about abnormal power consumption, due to terminal misreporting or too much useless information, it cannot achieve the purpose of really quickly and accurately locating suspected users of electricity theft and leakage and metering failures. at a loss

Moreover, when using this method to model, the determination of the weight of each input index of the model requires the knowledge and experience of experts, which is highly subjective and has obvious defects, so the implementation effect is often not satisfactory

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