Electronic transformer error prediction method

Abstract

The invention discloses an electronic transformer error prediction method. The method comprises: collecting error data and environmental parameter data of an electronic transformer to generate sampledata and rejecting abnormal data among the collected data; on the basis of a Z-score standardization method, carrying out standardization processing on the sample data; carrying out clustering processing on historical data of environmental parameters and establishing an electronic transformer error prediction model by training and learning; and according to the environmental parameter values, predicting a ratio error and an angular difference of the electronic transformer based on a prediction model. The method has the following advantages: no physical model needs to be established; on the basis of the multi-dimensional data driving method, the online estimation of the electronic transformer error can be realized according to the error data and environmental parameter data of the electronic transformer, so that a problem that no deterministic function relation exists between the error and the environmental parameter of the electronic transformer is solved and the accuracy of electronictransformer error prediction is improved.

Description

technical field [0001] The invention belongs to the field of status evaluation and fault diagnosis of power transmission and distribution equipment, and more specifically, relates to a method for predicting errors of electronic transformers based on clustering neural networks. Background technique [0002] The electronic transformer is one of the key equipment for the digitization, automation, informatization and interaction of smart substations. After the electronic transformer is adopted, the optical fiber of signal transmission fundamentally solves the problem of additional error in the transmission of the electromagnetic transformer to the secondary equipment, and greatly improves the accuracy of the measurement and metering system. However, from the point of view of field operation problems, the accuracy of electronic transformers still occupies a large proportion. Although all the electronic transformers in operation have passed the type test and factory test, their e...

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Problems solved by technology

However, this method does not consider the influence of outliers in the sample data on the structure and parameters of the model, and the model also has shortcomings such as easy to fall into local minimum and slow convergence.

[0006] To sum up, the existing technology cannot evaluate the long-term operation error of electronic transformers, and it is difficult to implement on-site, requires heavy labor operations, is greatly affected by the external environment, and the on-site prediction results may have large errors and are unreliable, etc.

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