Method for locating faults in power communication network

Abstract

The invention discloses a method for locating faults in a power communication network. The method comprises the steps that a bipartite graph model is built according to the many-to-many uncertainty of faults and symptoms; fault effect weight factors are led in on the basis of the bipartite graph model; the fault effect degree is calculated and corrected through credible parameters to obtain suspected fault sets. According to the method, the many-to-many uncertainty of faults and symptoms is modeled through a weighting bipartite graph, so that the causal relationship between the faults and the symptoms is expressed, the modeling capacity is good, noise immunity is high, and the method can adapt to real environment with fault burstiness and network complexity. The fault effect weight is led in, the total probability and the Bayes thought are utilized under the bipartite graph model, prior fault probability is transformed into condition probability, and the fault effect degree is calculated; finally, credible parameters are added to control effect of suspected faults, the cover degree and the contribution degree are combined, and the suspected fault sets of which the effect degree is within the controllable parameter range are picked out.

Description

technical field [0001] The invention relates to the technical field of communication, in particular to a fault location method in a power communication network. Background technique [0002] With the rapid development of the smart grid, the smart power communication network carries important information exchange and plays a very important role in the diversification of functions and the collaborative work of distributed power plants. Unguaranteed communication performance not only limits the use of electric energy and service quality of the smart grid, but also has potential damage to the information decision-making system. Network failure refers to the fundamental reason that causes the network to be in an abnormal state, business applications to be in an unavailable state, or a state of performance degradation. Faults are usually not directly observed by the network management system, but often manifested through some external phenomena or alarms. Network fault location ...

AI Technical Summary

Problems solved by technology

However, the codebook method requires a deterministic model structure between faults and symptoms. The above-mentioned codebook construction of fault-symptom deterministic model structure seeks the shortest distance between codewords, and the probability between faults and symptoms is lost. information, which makes the process of fault location more complicated, which affects the actual fault location effect

The non-deterministic relationship between faults and symptoms is not correctly expressed, and in the fault location process, the impact of deleting suspected faults is not considered, which may cause the deleted faults to be ignored by the fault location algorithm, thereby causing power accidents

[0004] Therefore, in the process of locating faults in the power communication network, the above method has certain limitations because it constructs a fault-alarm tree for existing faults, and it takes a long time, which affects the efficiency of positioning. In addition, because the impact of deleting suspected faults on the positioning results is ignored, the uncertainty is high, resulting in low accuracy of the fault positioning results

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