ANN and ES integration-based substation alarm information analysis and decision-making system and method

Abstract

The invention relates to an ANN and ES integration-based substation alarm information analysis and decision-making system and method. The system includes a database, a reasoning module, an explanation module, an implicit knowledge extraction module, a knowledge base and a man-machine interface module, wherein the database is used for storing real-time alarm information and equipment static configuration data which are received by the system, the reasoning module coordinates and controls the reasoning process of the alarm information, the explanation module explains the reasoning result of the reasoning module, the implicit knowledge extraction module is used for explicitly expressing knowledge expressed by numerical values, the knowledge base is used for storing explicit knowledge rules and implicit knowledge contained in connection weights, and the man-machine interface module is used for converting information outputted by the explanation module and the implicit knowledge extraction module into a form which can be understood by a user and outputting the form. According to the system of the invention, numerical value operation-based reasoning in an ANN is introduced into an ES system, so that single symbol-based expression of a traditional ES can be replaced, and therefore, the implementation efficiency of the ES can be improved; different analysis strategies are taken according to different faults; and therefore, the maintainability and fault-tolerance of a whole system can be improved, and the diagnosis range of the alarm information analysis and decision-making system can be also extended.

Description

technical field [0001] The invention relates to a system and a method, in particular to a substation alarm information analysis and decision-making system and method based on the integration of ANN and ES. Background technique [0002] With the increasing scale of the power system, various potential safety hazards are becoming more and more serious, especially the probability of simple accidents causing large-scale power outages and even regional power grid paralysis is increasing. Under the complexity and real-time requirements of the modern power grid, when the system fails, it is necessary to quickly detect the fault, accurately judge the fault, reasonably eliminate the fault, and reduce the scope of power outages to maintain the stable operation of the entire power grid. Therefore, how to quickly find the fault location and identify the fault category after a substation fault occurs is the key to dealing with power outages. [0003] In order to quickly and accurately ju...

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Problems solved by technology

[0004] In the past, after the substation was unattended, all the information was aggregated to the monitoring center and displayed in chronological order without any reasoning and judgment processing.

In recent years, the "big operation" system has been proposed to achieve the integration of regulation and control, and the integration of dispatching business and equipment monitoring business. Therefore, it is necessary to send a large amount of substation data information, various signal actions are frequent, and the monitoring tasks of operators are heavy. It is easy to miss important alarm signals, thus delaying processing and causing accidents

Once an accident occurs, it will not only aggravate the sharp increase of semaphore, but also scroll and swipe the screen, making the operating personnel even more dazzled and at a loss, making it difficult to grasp the key points and affecting the correct handling of the accident

[0005] At present, a lot of research work has been done on the analysis and processing of alarm information and fault diagnosis at home and abroad, and many methods have been proposed, such as artificial neural network, expert system, Petri network, data mining, Tabu search, etc. When performing fault analysis in substations, there will always be problems of inference matching conflicts and poor fault tolerance, which may easily lead to misjudgments or missed judgments. In addition, in the power system fault diagnosis, most of the research is on the overall fault diagnosis of the power system or a specific component. On the other hand, the research on the fault tolerance of the fault diagnosis system is not enough, especially the fault diagnosis system that uses the expert system as the core of the diagnosis has almost no research on the fault tolerance, and at the same time, the operation mode of the substation is not considered. The impact on the network structure, and the artificial neural network has not been practically applied in the fault diagnosis of the power system, and most of them can only be used as an offline auxiliary tool

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