Multiple state variable prediction-based voltage transformer state assessment method

Abstract

The invention discloses a multiple state variable prediction-based voltage transformer state assessment method comprising the following processes: main fault state variables of a voltage transformer are chosen; the chosen main fault state variables of the voltage transformer are classified into four types: static parameters, dynamic parameters, quasi-dynamic parameters and external parameters; based on the classified main fault state variables of the voltage transformer, a state variable prediction algorithm is adopted for predicting a state of the voltage transformer, and a predicted state value can be obtained; a neural network prediction algorithm is adopted for determining a trend of an operation state of the voltage transformer to be assessed, and a comprehensive assessment result of a voltage transformer state is given based on a real time operation state and a real time trend of the voltage transformer. The method disclosed in the invention is advantageous in that a large amount of state information of the voltage transformer can be subjected to comprehensive analysis, and potential faults of the voltage transformer can be discovered and predicted.

Description

technical field [0001] The invention relates to the technical field of electric equipment state evaluation, in particular to a transformer state evaluation method based on multi-state quantity prediction. Background technique [0002] During the "13th Five-Year Plan" period, the reform of China's electric power system will continue to deepen, and power supply companies must meet the needs of users with more secure, reliable, and high-quality services. With the development of power grid structure and the deepening of power grid reform, the distribution range of substations of various voltage levels is becoming wider and wider, and the number is also increasing sharply. There are also more and more power equipment corresponding to it. Therefore, the overhaul and maintenance of key substation equipment is more complicated, and the accuracy of equipment health status analysis and grid reliability standards are getting higher and higher. [0003] The main sources of substation e...

AI Technical Summary

Problems solved by technology

However, the existing equipment status evaluation and diagnosis models are mainly based on the analysis and judgment of a single or a small number of status parameters, most of which are limited to the scope of threshold diagnosis, which cannot fully grasp the real health status and operation risks of equipment, and the comprehensive application level of information is low. , cannot make full use of a large amount of equipment state information for comprehensive analysis, the state evaluation results are one-sided, and at the same time cannot fully reflect the objective law between fault evolution and performance characteristics, and it is difficult to discover and predict latent faults

[0004] Transformer fault state information is extremely diverse, such as preventive tests, records of bad operating conditions, family defect records, maintenance records, self-quality records, online monitoring, etc. If all fault state information is considered, the state evaluation system will be extremely complicated, and at the same time Some state information is relatively vague, which is not suitable for quantitative description, which is not conducive to a comprehensive and accurate evaluation of transformers. In addition, the inspection / monitoring data collection range is wide and the value density is low.

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