Intelligent Hybrid System Based On Data Mining For Distribution System Fault Diagnosis

The reliability and power quality of power system is directly related to national economic development and the level of people daily lives elevation. When the distribution network failures, especially when the protection and circuit breaker tripping or malfunction occurs and signal changes or loss caused by communications device failure, it will form variation failure mode which result in complication of alarm mode and bring more difficulties to the distribution system fault diagnosis. Practicality of existing research methods is subject to certain restrictions because when it deal with variation mode, it is difficult to guarantee the high fault tolerance and there will be miscarriage of justice. So it has become a very meaningful research topic to determine fault,providing a basis to disconnection and restoration of power supply and reducing the outage cost when the distribution network system failures.In this article, i use hybrid data mining methods that combine rough set approach and neural network in order to overcome the defect of single data mining method in fault tolerance and low speed that exist in distribution system fault diagnosis. Based on the analysis and study predecessors for the distribution network fault diagnosis, i research on a variety of artificial intelligence methods used in distribution network fault diagnosis and analyse the characteristics and the main problems existence in distribution network fault diagnosis. Using hybrid data mining method in the distribution network fault diagnosis, firstly seeing protecting the circuit breaker as a condition attribute and the regional distribution network as a decision attribute.At the same time, examine a variety of fault conditions and the establishment of decision-making table the failure to study a variety of fault conditions and the establish decision-making table.Then, using attribute reduction algorithm that combinaed with Boolean logic and discernibility matrixcan to reduction an example decision table constructed and delete unnecessary attributes. Using artificial neural network that called the most simple set of rules for learning and training, which is not only reduces the time that neural network training learn, but also improve diagnostic accuracy. This method takes advantage of knowledge reduction capacity of rough set theory and fault-tolerant ability of artificial neural networks. It improves the ability of generalization and make up for their shortcomings.At last, this article use VB language as development tool and using Matlab neural network tool box to establish a simplified distribution network fault diagnosis system. It is verified through the distribution network examples that the method is correctness and effectiveness.