Equipment state big data computing method and equipment based on Goldstein-BP algorithm

Abstract

The invention discloses an equipment state big data computing method based on a Goldstein-BP algorithm. The method comprises the steps that S101, historical data and current data of grid equipment state evaluation indexes and grid equipment historical state evaluation results are acquired; S102, a corresponding BP neural network model is constructed according to equipment state index historical data characteristics; S103, a mean square error function is constructed according to the historical data of the grid equipment state evaluation indexes and the grid equipment historical state evaluationresults; S104, a Goldstein method is adopted to obtain a minimum mean square error, a learning rate in the BP algorithm is solved, and BP neural network parameters are computed through the BP algorithm; S105, the BP neural network parameters are substituted to obtain a BP neural network model; and S106, current equipment state evaluation index data is used as input of the BP neural network modelto obtain an output result. Through the technical scheme, the Goldstein method is adopted to solve the optimal learning rate of the mean square error function in the BP algorithm, and therefore the precision level of grid equipment management is raised.

Description

technical field [0001] The present invention relates to a large data calculation of power grid equipment state, in particular to a method and equipment for calculating large data of equipment state based on the Goldstein-BP algorithm. Background technique [0002] The state evaluation of power grid equipment refers to the analysis and evaluation of various state quantity indicators reflecting the health state of equipment based on the results of operation inspection, maintenance, overhaul, preventive test and live test (online monitoring), so as to determine the equipment state level and state classification. value. The status evaluation of power equipment is the basis for risk assessment of power equipment and power grid operation, and many decisions in the entire life cycle of assets involve the calculation of risks, or the balance of benefits and risks. Therefore, the status evaluation of power grid equipment plays a fundamental role in equipment investment planning, mat...

AI Technical Summary

Problems solved by technology

However, when the traditional BP neural network solves the mean square error function, it generally uses the principle of fixed learning rate to iterate. This will cause the algorithm to not converge when the learning rate is too large, and the algorithm will learn if it is too small. The problem of low efficiency

At present, there are a lot of equipment state index data involved in power grid equipment, and the model is relatively complex, and the results are more difficult to control, and it is difficult to adapt to the requirements of the power grid equipment state evaluation method under the big data

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