BP neural-network algorithm-based status evaluation method of relay protection equipment

Abstract

The invention discloses a BP neural-network algorithm-based status evaluation method of relay protection equipment. The method includes: constructing neural units of a status evaluation model on the basis of classical reliability theory and historical maintenance data, carrying our training and learning on the basis of a large number of samples, and forming a status evaluation model algorithm through a BP algorithm; adopting the status evaluation model to estimate situations of an equipment running environment of the relay protection equipment and the like to obtain estimation results on the relay protection device; adopting the status evaluation model to estimate device circuit inspection situations of a running environment of a secondary circuit and the like to obtain estimation resultson the secondary circuit; and obtaining an overhaul range according to the estimation results of the relay protection device and the secondary circuit. By applying the embodiment provided by the invention, quantitative analysis, monitoring and early warning are carried out on the relay protection equipment through the perfect estimation and evaluation method of the relay protection device and thesecondary circuit and combination of the data model of the relay protection equipment.

Description

technical field [0001] The invention relates to the field of power systems, in particular to a method for evaluating the state of relay protection equipment based on a BP neural network algorithm. Background technique [0002] Maintenance of relay protection equipment is the performance maintenance of relay protection equipment in the grid system to ensure its reliability. The traditional maintenance of relay protection equipment adopts a regular inspection mode. With the development of modern power grid scale and technology, this maintenance mode has become less and less suitable for the needs of power grid development. [0003] According to the traditional regular maintenance mode, the maintenance period of the equipment is fixed. Regardless of the comprehensive status of the equipment, it must be shut down for maintenance when it expires. The scientific nature of the equipment maintenance plan is not strong; and the traditional maintenance method must be limited Many mai...

AI Technical Summary

Problems solved by technology

[0003] According to the traditional regular maintenance mode, the maintenance period of the equipment is fixed. Regardless of the comprehensive status of the equipment, it must be shut down for maintenance when it expires. The scientific nature of the equipment maintenance plan is not strong; and the traditional maintenance method must be limited Many maintenance items are completed with

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