With the rapid development of smart grid, intelligent electric meters can be seen in most of the households, and the volume of electric energy data is in a rapid growth. This paper mainly aims at introducing an abnormity diagnosis platform in electric power system. It is used to distinguish the abnormal point according to the historical data and expert experience, and put forward some resolving scheme to ensure the high reliability and stability of power grid. In our approach, we use distributed technologies to process big electric energy data. Specifically, distributed fie system (HDFS) and distributed database (HBase) are applied to data storage, and distributed computing technology (MapReduce) is applied to constructing knowledge base and computing. In the inference engine, we use Hidden Semi-Markov Model. This model can auto-get and modify knowledge in knowledge base, achieve a better real time phenomenon, through self-learning function and machine as well as interacting between human. The results show that this abnormity intelligent diagnoses platform is effective and faster.

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A real-time intelligent abnormity diagnosis platform in electric power system

Along with the rapid development of information technology, an increasing number of data need to be collected from the building electrical system. Therefore, it is critical to design a data acquisition system can be directly integrated with the existing building control system. In this paper, we concentrate on the research of fault diagnosis data collection. We design a MODBUS based data acquisition system to collect the fault signals involving current, voltage and harmonic, which can provide real-time and accurate fault data for the analysis and calculation in the building electrical fault diagnosis system.

Design of a data acquisition system for building electrical fault diagnosis

In this paper, a test system hardware platform based on PXI bus system is proposed to test the airborne power system and typical power distribution structure of large-scale Unmanned aerial vehicles (UAV). By researching system structure, analyzing failure mode and designing fault tree, fault diagnosis expert system is designed to test the airborne power system in large-scale UAVs, where a forward reasoning expert knowledge base based on extension rule is built to solve the non-exact inference in complex system via introducing certainty factor. The experiments illustrate that the proposed system can effectively improve the intelligent level of test system and has good application prospects.

Fault Diagnosis Expert System for Electric Power System of the Large-Scale UAVs Based on Virtual Instrument

This paper introduced the fault diagnosis technology and analyzed the shortcomings of traditional fault diagnosis method. Then simply introduced the intelligence fault diagnosis expert system. At last respectively introduced the design idea of three intelligence fault diagnosis expert systems, they respectively is used in rotating machine, mechanical maintenance and hydraulic system. At the same time the key technologies of each intelligence fault diagnosis expert system was respectively introduced.

Fault Diagnosis Expert System Applied on Machinery Industry

Based on the theory of expert systems,simulating thoughts process of experts,gathering domain knowledge of all experts and according to the problem characteristics of application and management system,an expert systems model of malfunction intelligent diagnose has been built up.In the system,the method of knowledge representation is genetic rule,the inference engine combines uncertain inference and rule inference.In order to deal with the uncertain knowledge,an uncertain inference model based on confirmation theory is established.This model can auto-gets and modify knowledge in knowledge base,achieve a better real-time characteristics,through interacting between human and machine as well as self-learning function.

An Expert Systems Model of Malfunction Intelligent Diagnose

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An Expert Systems Model of Malfunction Intelligent Diagnose