Industrial process modeling method for evaluating and governing sag and short-time interruption severity

Abstract

The invention provides an industrial process modeling method for evaluating and governing sag and short-time interruption severity. The method comprises the following steps: constructing a failure probability transfer function model of sensitive equipment, and constructing a failure probability transfer function model of a sensitive module influenced by a voltage sag by utilizing a VTC curve; carrying out describing by using voltage amplitude uncertain region boundaries VI and VII and duration uncertain region boundaries TI and TII withstood by the equipment; because of independence of the amplitude and the duration characteristic, depicting the probability of equipment failure caused by one sag / short interruption event by the product of the probabilities of equipment failure caused by allthe characteristics. According to the model provided by the invention, the traditional model deviation caused by modeling based on typical research results or experimental parameters is reduced, theaccuracy of the model and the credibility of evaluation results are improved, a reasonable basis can be provided for the design of a user voltage sag treatment scheme, and a large number of economic losses caused by voltage sag are avoided for a user.

Description

technical field [0001] The invention belongs to the field of electric power technology, and in particular relates to a construction method for constructing a model of the influence of voltage sag and short-term interruption on users in the electric power industry model. Background technique [0002] With the development of social economy, a large number of control devices such as frequency converters, programmable logic controllers, and contactors have entered the production process of industrial users. These devices have improved the production capacity of users while also improving the quality of power supply in the production process. . A voltage sag / short-term interruption may cause the above-mentioned sensitive equipment to trip, and then trigger a chain trip, interrupting the industrial process and having a huge impact on the user's production. [0003] In order to reduce the sensitivity of users' industrial processes to voltage sags and short-term interruptions, the ...

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Problems solved by technology

[0009] In the prior art, the VTC parameters of the user process interruption probability assessment board and the logical relationship structure between the equipment and the process are mainly determined based on standard research results or typical experiments carried out on the same type of equipment, which are not well compared with samples that reflect the actual user production line The combination of data leads to a large deviation between the evaluation results of the constructed model and the actual results

For example, the parameters of the VTC model are usually determined based on existing research results, which may not reflect the tolerance characteristics of sensitive equipment in the actual industrial process environment; the logical relationship model between equipment and processes is usually presented in the form of series-parallel modules or fault trees , but these models based on graphical logic structures are only suitable for describing simple "and" and "or" logic, and cannot reflect the various logical relationships contained in the actual industrial environment, and it is also difficult to guarantee objectivity

The existence of the above problems will lead to large differences between the industrial process model and the actual situation, resulting in large deviations in the evaluation results, making it difficult to provide effective reference

[0010] In the prior art, the VTC parameters of the user process interruption probability assessment board and the logical relationship structure between the equipment and the process are mainly determined based on standard research results or typical experiments carried out on the same type of equipment, which are not well compared with samples that reflect the actual user production line The combination of data leads to a large deviation between the evaluation results of the constructed model and the actual results

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