Grid fault diagnosing method based on combined type cross entropy algorithm

Abstract

The invention provides a grid fault diagnosing method based on the combined type cross entropy algorithm. The method comprises the steps of collecting the action messages of a protector and a breaker by means of a data acquiring and monitoring control system and a relaying information system, marking off faulty sections and determining a suspended fault element set, establishing an objective function based on suspended fault elements and solving the function by means of the cross entropy algorithm, identifying a fault element through the obtained optimal solution, and outputting a diagnosing result. According to the grid fault diagnosing method based on the combined type cross entropy algorithm, a fault diagnosing model is established according to the action messages of the protector and the breaker, the optimal solution of the model is obtained by means of the cross entropy algorithm, and then the fault element is identified. The grid fault diagnosing method based on the combined type cross entropy algorithm can be used for on-line grid fault diagnosis.

Description

technical field [0001] The invention relates to a grid fault diagnosis method. Background technique [0002] With the continuous expansion of the scale of the power grid and the gradual increase of equipment, there will inevitably be various faults in the power grid, and because the power grid is widely distributed, once the system fails, it is not conducive to the actual search. Therefore, it is of great significance to study the problem of power grid fault diagnosis to realize automatic identification of fault components. The diagnosis method using the traditional mathematical model cannot guarantee the accuracy and rapidity of the diagnosis to a large extent. In comparison, the diagnosis method based on intelligent technology has obvious advantages. Therefore, the fault diagnosis method has changed from traditional technology to intelligent The development of technology is an important trend in the future development of this field. Intelligent technologies for power gri...

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Problems solved by technology

[0003] However, when the particle swarm algorithm solves discrete problems such as power grid fault diagnosis, it is easy to fall into local optimum.

The genetic algorithm adopts binary coding, so there is a Hamming Cliff problem, and at the same time, if Figure 8 — Figure 9 As shown, the results of the genetic algorithm are highly dispersed, and the optimal solution cannot be obtained stably.

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