Transformer state knowledge acquisition method and equipment based on genetic algorithm and attribute support

Abstract

The invention discloses a transformer state knowledge acquisition method and equipment based on genetic algorithm and attribute support degree. The method establishes a decision table through transformer fault cases, combines the genetic algorithm and attribute support to solve the state knowledge under the abnormal information of the specific state of the transformer, and finds the evolution law of the transformer state. Compared with the traditional method, the calculation complexity is greatly reduced, which is conducive to efficient extraction and decision-making The most relevant condition attributes in the table for the type of transformer fault. This method can overcome the shortcomings and deficiencies of the existing technology, and can be widely used to understand the development law of transformer status, realize transformer status evaluation, guide transformer status monitoring and formulate differentiated operation and maintenance strategies, and the method is simple and efficient, which is conducive to improving power consumption The operation of the transformer is safe.

Description

technical field [0001] The invention relates to fault diagnosis and state evaluation of power transformers, in particular to a transformer state knowledge acquisition method and equipment. Background technique [0002] In recent years, under the background of the energy Internet, with the large-scale development of the national smart grid, the application of various state sensing and detection technologies has become more mature, and intelligent detection and analysis technologies have begun to emerge in various fields of the power grid, such as smart patrolling. Inspection robots, unmanned inspection helicopters, and intelligent analysis and diagnosis systems, etc., my country's power grid has initially possessed the ability to grasp real-time data such as power equipment operation status, environmental changes, and operation inspection information through information technology. Diagnosis and risk prediction provide the basis. As one of the important equipment in the power ...

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Problems solved by technology

However, this kind of method relies heavily on the learning algorithm, and the algorithm input requires a lot of state information, and the relationship between each state information is unclear, and it is impossible to effectively understand the influence of each state information on the transformer fault; (3) Comprehensive evaluation: In order to Indicate the location and even the severity of the fault, and use as comprehensive information as possible to obtain a more refined transformer state conclusion by introducing fuzzy mathematics, rough set and other theories

However, at present, the above methods only focus on transformer fault diagnosis and fault cause analysis.

In addition, in the existing transformer state evaluation system, most of the research work only single-scores or simply weights the results represented by different state information, and seldom carries out the extraction and analysis of transformer state knowledge and the research on state development laws.

[0003] Looking at the existing transformer fault diagnosis and state evaluation methods, there are still the following problems: (1) The transformer fault diagnosis model relies on neural network algorithms, and the relationship between each state information is unclear, and it is impossible to effectively understand the impact of each state information on transformer faults It is not conducive to the extraction and analysis of the key state information of the transformer; (2) The existing information fusion method only calculates the weighted multi-dimensional state information of the transformer, and cannot find the difference between different abnormal state information, abnormal state information and different fault types. Progressive relationship, cannot efficiently form transformer state knowledge from fault cases

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