Power system fault diagnosis method based on timing order fuzzy Petri net

Abstract

The invention discloses a power system fault diagnosis method based on a timing order fuzzy Petri net (TOFPN). The TOFPN is defined as an eight-membered group according to the definition of a fuzzy Petri net (FPN) and the information timing order sequence, the reasoning decision of TOFPN and the reasoning decision of an ordinary fuzzy Petri net are the same, a practical uncertain reasoning method, namely an MYCIN confidence coefficient method is adopted, two operators (please see the two operators in the specification) in a maximum algebra are introduced in the method. By means of the power system fault diagnosis method based on the TOFPN, power system fault diagnosis on alarm information time order attributes under the situation of protective circuit breaker maloperation, refusing and information losing is achieved. According to the method, the diagnosis speed is high, precision is high, the action evaluation of a protective circuit breaker can be accurately finished, high adaptive capacity for the power grid topological change is achieved, and the method is suitable for diagnosing faults of a large complex power grid and has good application prospects.

Description

technical field [0001] The invention relates to a power system, in particular to a power system fault diagnosis based on time series fuzzy Petri net. Background technique [0002] At present, the fault diagnosis of power system components is mainly based on the protection and circuit breaker action information obtained from the data acquisition and monitoring (SCADA) system. The main methods include optimization algorithms, artificial neural networks, expert systems, fuzzy reasoning, and Petri nets. The above method has certain adaptability in component fault diagnosis, but still has the following limitations: Most of them rely on the accurate and complete fault information of the dispatching center. Accurate diagnosis results, especially when multiple faults or expanded faults occur, the problem is particularly prominent; in the process of power system faults, the timing attributes of information have not been fully and reasonably used; for fault diagnosis of large-scale an...

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Problems solved by technology

The above method has certain adaptability in component fault diagnosis, but still has the following limitations: Most of them rely on the accurate and complete fault information of the dispatching center. Accurate diagnosis results, especially when multiple faults or expanded faults occur, the problem is particularly prominent; in the process of power system faults, the timing attributes of information have not been fully and reasonably used; for fault diagnosis of large-scale and complex power grids, how to After the network topology changes, realizing the automatic correction of the diagnosis model is also one of the key problems to be solved urgently.

[0003] In recent years, some scholars at home and abroad have used methods such as information theory and rough sets to solve the problem of information uncertainty in fault diagnosis of large-scale and complex power grids, and used methods such as Petri nets and Bayesian networks to model components to solve the problem of large-scale power grids. These studies have made some progress in modeling complex problems in the diagnosis process, but there are still some difficulties in the diagnosis modeling and decision-making of large-scale complex power systems

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